Smoking Among Chronically Homeless Adults: Prevalence and Correlates

Of 754 participants who responded at baseline, 152 (20%) reported that in the past three months they had not smoked, 290 (38%) reported smoking but limiting themselves, and the largest group of participants, 312 (41%), reported regularly smoking with no limitations. A total of 449 participants (60%) reported that they discussed smoking with a health care provider in the past year. Thus, among the 602 participants who reported smoking, 48% tried to limit their smoking and 75% discussed smoking with a health care provider.

Several univariate differences in baseline characteristics were found between the three smoking groups (Table 1 and Table 2). The self-limiting smokers were older than the regular smokers; the regular smokers reported more years of lifetime incarceration and were more likely to be white than the self-limiting smokers; and the self-limiting smokers reported more years of incarceration than the nonsmokers. Nonsmokers had higher SF-12 physical component scores (indicating better health) than self-limiting and regular smokers. But there were no differences in diagnoses of mental disorders (excluding substance abuse diagnoses), employment, income, residential history, or quality of life.

Compared with nonsmokers, regular smokers and self-limiting smokers were more likely to have a diagnosis of alcohol abuse or dependence, a diagnosis of drug abuse or dependence, a dual diagnosis, and higher scores on the ASI drug subscale. Among the three smoking groups, regular smokers had the highest scores on the ASI alcohol subscale and self-limiting smokers reported a greater number of medical outpatient visits in the three months before baseline. As expected, both self-limiting smokers and regular smokers were more likely to have discussed smoking with a health care provider in the past year.

A stepwise multinomial regression was conducted to examine the independent association between significant baseline variables and smoking group at baseline. Similar significant risk factors for smoking were found for self-limiting and regular smokers (Table 3). A diagnosis of an alcohol or drug use disorder, a lower SF-12 physical component score, older age, more years incarcerated, and a higher score on the ASI drug subscale significantly increased the likelihood of being either a self-limiting smoker or a regular smoker. Scores on the ASI drug subscale had the strongest association with being a smoker (odds ratio [OR]=1.75 for self-limiting smokers, OR=2.06 for regular smokers).

Over the one-year study period, no significant change was found in the proportion of participants in each smoking group. Across time points, nonsmokers accounted for 20%–22% of participants, self-limiting smokers for 37%–43%, and regular smokers for 37%–41%. The results were similar when data were analyzed for only participants who provided data at both baseline and 12-month follow-up (N=582). Table 4 shows the proportion of participants in each smoking group at baseline in terms of their smoking group status at 12-month follow-up. Most nonsmokers at baseline remained nonsmokers at 12 months, and most smokers at baseline remained smokers at 12 months, although the proportions of self-limiting smokers who became regular smokers and of regular smokers who became self-limiting smokers were about 30%.

Given the strong associations at baseline between smoking group and scores on the ASI alcohol and drug subscales, we examined these associations over time. When the analysis controlled for baseline values of these variables, changes in ASI alcohol scores and ASI drug scores were both positively associated with changes in smoking status over a one-year period (r=.12, p=.005, and r=.12, p=.003, respectively). On the basis of observations from all time points over one year, the ASI alcohol and drug scores were also both positively associated with smoking status (r=.17, p<.001, and r=.18, p<.001). This was confirmed by a linear mixed regression analysis, which found that when the analysis controlled for baseline ASI alcohol scores, smoking group was significantly associated with ASI alcohol scores over time (F=17.26, df=2 and 2,565, p<.001). Nonsmokers had an ASI alcohol least-square mean score of .09±.01; for self-limiting smokers this score was .10±.00, and for regular smokers it was .13±.00. A similar effect was seen for smoking group on the ASI drug scores over time (F=16.66, df=2 and 2,564, p<.001) when the analysis controlled for baseline ASI drug scores. Nonsmokers had an ASI least-square mean score of .04±.00; for self-limiting smokers this score was .05±.00, and for regular smokers it was .06±.00.

To examine predictors of quitting or limiting smoking over time, a stepwise binary logistic regression was conducted using all baseline variables and change scores on the ASI alcohol and drug subscales to predict over one year either quitting or becoming a self-limiting smoker versus making no change or smoking more. In the final model, the only significant variables were a diagnosis of posttraumatic stress disorder (b=1.21, SE=.45, p=.007, OR=3.36) and change score on the ASI alcohol subscale (b=.26, SE=.11, p=.020, OR=1.30) (Figure 1).

This study found that among a large sample of chronically homeless adults, 80% reported cigarette smoking in the past three months. This point prevalence rate is similar to but slightly higher than previous estimates among homeless adults (13,14) and among people with severe mental illness (3–5). The findings of this study suggest that smoking may be even more prevalent in the chronically homeless population, although a more representative sampling may be needed.

Among homeless smokers, about half reported that they tried to limit their smoking, and accordingly, more than half reported that they had discussed smoking with a health care provider in the year before entering supported housing, which is similar to estimates in previous studies (13,15). This finding is encouraging because it suggests that most homeless clients have at least been asked by their providers about smoking, which casts some doubt on the belief that health care providers delay addressing smoking in order to address more salient primary mental health and psychosocial problems (7). However, this study did not document whether homeless smokers were referred to or received any smoking cessation treatment. There has been virtually no research on smoking interventions targeting homeless people, although preliminary data from pilot studies have shown that cognitive-behavioral therapy, motivational interviewing, and pharmacotherapy are feasible and may be effective (25–27). Development and empirical evaluation of specific interventions for this population are needed.

One question generated by the findings is how homeless individuals maintained their nicotine use with such limited incomes. The cost of cigarettes can be prohibitively expensive, especially for those who are regular smokers. It is not clear how or whether the study participants paid for their cigarettes. A prior study found that homeless adults often shared cigarettes with others, smoked remade cigarettes, or smoked discarded cigarette butts and filters (28). Further investigation is needed on how much money homeless people spend on cigarettes and whether increases in income may lead to increased spending on tobacco, as one study found among beneficiaries of Social Security Disability Insurance (29). In those cases, money management interventions, which have been found to be somewhat effective for alcohol and drug abuse (30), may be helpful in promoting nicotine cessation, although this needs to be empirically studied. When correlates of smoking were examined, alcohol use and especially drug use showed some of the strongest independent associations. This is consistent with findings from the Health Care for the Homeless User Survey (13) and is not surprising given the high prevalence rate of smoking in the general population among persons who abuse alcohol and drugs (31,32). Being older, having more lifetime years of incarceration, and poorer physical health also increased the likelihood of being a smoker, and similar correlates were found for those who smoked but tried to limit their cigarette use and those who smoked without monitoring their use. Gender, education, and socioeconomic status were not associated with smoking, contrary to findings for adults in the general population (1). This may reflect the fact that most chronically homeless adults are male with low levels of education and from low socioeconomic backgrounds. Being in a supported housing program had no effect on smoking behavior over time, which is not surprising because the programs did not focus on providing smoking cessation treatments. However, supported housing programs should consider integrating tobacco cessation services with mental health treatment because this approach has been found to be effective (8–10). Change in smoking behavior was found to be associated with change in alcohol and drug use over time, suggesting that tobacco cessation treatment should be provided concurrently with substance abuse treatment, as other researchers have argued (33,34). A meta-analysis has shown that smoking cessation interventions provided concurrently with addictions treatment can enhance sobriety (35). There has been limited research on what types of smoking interventions are most effective for the homeless population, but chronically homeless adults are likely to be more receptive to approaches that take into account their stage of change (36) because they may be more focused on a variety of other problems.

Multivariate analyses showed not only that reductions in alcohol use predicted quitting or limiting smoking over time but also that participants who had PTSD were more likely to quit or limit their smoking. It is not clear why PTSD would be associated with reduced smoking; however, this finding may be reframed as reflecting the fact that people with severe mental illnesses (schizophrenia, bipolar disorder, or major depression) were less likely to quit or limit their smoking than those with PTSD. Some studies have shown that individuals with severe mental illness have a particularly hard time quitting smoking (37,38) and may need additional support and assistance.

This study had several limitations worth noting. Most measures, including those of smoking behaviors, were based on participant self-report. Smoking behaviors were assessed simply, and no data were collected on intent to quit smoking or attempts to quit. The study design was observational, and significant findings were largely associational; therefore, directionality and causality cannot be inferred. A large number of analyses were conducted, which increases the possibility of type I error, although it is notable that the multivariate analyses included only significant variables identified beforehand. Further replication and intervention studies are needed to understand how to best reduce mortality from smoking-related diseases among homeless people.

This study found that alcohol and drug use, which are major risk factors for homelessness, were associated with smoking among chronically homeless adults, suggesting that smoking cessation treatment should be integrated with substance abuse treatment. More than three-quarters of chronically homeless adults in the study reported current smoking, and about half of homeless smokers tried to limit their cigarette use and had spoken to a health care professional in the past year about smoking. However, there are virtually no smoking cessation interventions specific to homeless people. This study demonstrates the need for more attention to smoking among chronically homeless adults because, even though quitting smoking must compete with a range of other goals for homeless adults, smoking is the most preventable cause of death.

The CICH Funder's Group, representing the U.S. Department of Housing and Urban Development, the U.S. Department of Health and Human Services, and the Department of Veterans Affairs, provided essential support and guidance to this evaluation. This material is also based on work supported by the Office of Research and Development, Veterans Health Administration. The views presented here are those of the authors and do not represent the position of any federal agency or of the United States government.

The authors report no competing interests.