Do Common Mental Disorders Increase Cigarette Smoking? Results from Five Waves of a Population-based Panel Cohort Study

Khalida Ismail1, Andy Sloggett2 and Bianca De Stavola3

1 Department of Psychological Medicine, Guy's, King's and St. Thomas' School of Medicine, London, England, United Kingdom.
2 Centre for Population Studies, London School of Hygiene and Tropical Medicine, London, England, United Kingdom.
3 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, England, United Kingdom.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A prospective panel cohort design was used to investigate whether mental disorders common in the general population increase the likelihood of increased cigarette smoking at 12 months follow-up. By 1995, the last year for which data were available, a random sample of 12,057 persons aged 16–75 years residing in private households in Great Britain had been recruited. At each of five annual waves, the main exposure, past mental disorder, was derived from assessments of psychiatric morbidity as measured by the General Health Questionnaire-12. Increased cigarette smoking was derived from observations of number of cigarettes smoked and was defined by an increase of five or more per day relative to the previous calendar year. After logistic regression analysis, persons with a common mental disorder were about 30% more likely to have increased their cigarette smoking over the previous year (odds ratio = 1.29, 95% confidence interval: 1.16, 1.43). The estimated effect in the youngest (16–21 years) and oldest (51–75 years) age groups was higher than that in the middle (31–50 years) age group (odds ratios = 1.50, 1.57, and 1.12, respectively; test for interaction, {chi}2=6.8 (3 df), p=0.078). These findings indirectly support the hypothesis that common mental disorders may have an enduring effect of increasing cigarette smoking a year later. Am J Epidemiol 2000;152:651–7.

logistic models; mental disorders; nicotine; prospective studies; psychiatry; smoking

Abbreviations: GHQ-12, General Health Questionnaire-12


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Despite extensive health education and taxation, the prevalence of smoking in Great Britain remains high at 27 percent (1Go). Approximately 3 percent of the population, especially teenagers, commence smoking annually (2Go, 3Go).

Smoking is the largest preventable risk factor for mortality (4Go) and is also associated with psychiatric disorders (5Go). It has often been suggested that some smokers start and maintain smoking to self-medicate against depressive symptoms, as the psychoactive effects of nicotine help to elevate their mood (6Go). Smokers resistant to quitting are more likely to be nicotine dependent (7Go). Major depressive disorders are twice as common in smokers than in nonsmokers (8Go) and are associated with nicotine dependence in young adults (9). Furthermore, cross-sectional associations between symptoms of depression and smoking have been found for women (10Go, 11Go), teenagers (12Go), and community adult populations (8Go, 13Go).

Psychiatric disorders are also associated with a 1.5–2.5 times increase in mortality in the general population, which cannot all be explained by unnatural causes of death, such as suicide (14GoGo–16Go). This excess mortality due to natural causes in people with psychiatric disorders could be partly explained by the association between psychiatric disorders and unhealthy lifestyles, such as smoking (17Go). It has been suggested that psychiatric disorders increase cigarette smoking (18Go), but the evidence for this association has yet to be established (19Go).

Findings from three US studies that have prospectively examined the association between smoking and depression have been inconsistent (20GoGo–22Go). Importantly, none of these studies controlled for important sociodemographic confounders. To the best of our knowledge, the contribution of mental disorders to smoking behaviors has not been examined prospectively in the British population, which may differ from US populations regarding smoking behaviors. In the present study, we used prospectively collected data on smoking, psychiatric morbidity, and relevant confounders from a large community cohort to examine the hypothesis that common mental disorders are associated with increased cigarette smoking at 12 months follow-up.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
British Household Panel Survey
We used data from the British Household Panel Survey (23Go), a longitudinal panel survey of private households in Great Britain (excluding Northern Ireland) that was started in 1991. It was designed to measure social, economic, and health behaviors of persons representative of the British population residing in private households. A panel is a sample of subjects for whom the same variables are measured repeatedly over several points in time, usually spaced equally. In our study, a set of social, economic, and health variables was measured at baseline (wave 1) and then annually for 4 more years (waves 2–5). Hence, a series of repeated measurements of several variables was available for each person; the number of repeated measurements depended on the number of waves in which the person was interviewed successfully. Smoking and mental health status data from previous waves were transferred to the succeeding wave, so that changes in cigarette smoking from one year to the next could be calculated.

Households were initially selected for inclusion in the panel survey by using a two-stage stratified sampling process. The sampling frame used was the Postcode Address File for Great Britain (excluding Northern Ireland). This frame is widely used in large government surveys (24Go).

During the first stage of sampling, 250 postcode sectors were randomly selected, with the probability of selection proportional to the size of the sector. Each postcode sector selected was called a primary sampling unit and contained on average 2,500 delivery points (equivalent to addresses).

During the second stage, an average of 33 delivery points were randomly selected from each primary sampling unit, with equal probability for selection. If one delivery point included more than three households, such as more than three flats in one building, then three households were randomly selected from the total number available. A private household was defined as one person living alone or a group of people who shared either living accommodations or one meal a day. Household members had to reside there for at least 6 continuous months a year so we could avoid recruiting students who lived at their parents' homes during vacations. Nonresidential addresses and institutions were excluded.

All adults aged 16–75 years residing in each household selected were eligible for entry into the survey at baseline, wave 1, and were defined as the original sample members. The sample for wave 2 and all subsequent waves consisted of all original sample members in all households identified in wave 1. In addition, new eligibility for the sample could occur in wave 2 and beyond if an original sample member moved into a household with one or more new people or one or more new people moved in with an original sample member. All new adults identified in the original sample member's household in subsequent waves were thus recruited into the study in that wave and were followed up in successive waves as long as they were still residing with the original sample member. Each successive wave thus consisted of all original sample members plus a small proportion of new members.

Measures
Evidence of common mental disorder was measured at each wave of the British Household Panel Survey by using the self-administered General Health Questionnaire-12 (GHQ-12) (25Go). This questionnaire was devised for screening those mental disorders common in the general population, predominantly depressive and anxiety disorders, and scores the subject's current mental health in relation to his or her usual state. A value of 3 (out of 12) is defined as the cutoff point at which a respondent is classified as suffering from a mental disorder (or is GHQ-12 positive). Although a specific diagnosis cannot be made by using the GHQ-12 alone, this questionnaire has a sensitivity and specificity of 89 and 80 percent, respectively, for a psychiatric diagnosis at this cutoff point (25Go). The exposure of interest in this study, past mental disorder, was defined as being GHQ-12 positive in the previous wave and was recorded in waves 2–5.

Information on smoking status consisted of the binary indicator current smoker or nonsmoker and the number of cigarettes smoked per day. Change in cigarette smoking was calculated as the difference in the number of cigarettes smoked between successive waves and therefore could be defined for waves 2–5 only. Increased cigarette smoking, the main outcome, was defined as present if the subject was smoking at least five cigarettes per day more than during the previous wave, and this information was generated for waves 2–5. We considered this increase the minimum associated with an increase in physical health risk. The definition of increased cigarette smoking included both new and regular smokers, and new smokers were defined as current smokers who were nonsmokers in the previous wave.

As there could not be a measure of GHQ-12 or number of cigarettes smoked before wave 1, there was no measure of past mental disorder or increased cigarette smoking for wave 1. Hence, the main analyses concerned changes in smoking status as recorded in waves 2–5. Whenever smoking was not recorded at a particular wave, the outcome indicator for the next wave was defined as missing. Likewise, whenever mental status had not been assessed at a particular wave, the exposure of interest for the next wave was defined as missing.

Other variables were investigated as potential risk factors and potential confounders of the association between past mental disorder and increased cigarette smoking. Included were sex; age at interview, categorized into the four age bands of 16–21, 22–30, 31–50, and 51–75 years; socioeconomic status, derived from the Registrar-General classification of current or previous occupational status (26Go) (if unknown, the occupational status of the head of household was recorded); education, classified as having gained university qualifications usually from the age of the early twenties onward, college qualifications (Advanced level/Higher National Diploma) usually at age 18 years, secondary or high school qualifications (Certificate of School Education, General Certificate of School of Education, Ordinary level) usually at age 16 years, or no academic qualifications; marital status, classified as married/cohabiting, divorced/separated, widowed, or never married; current alcohol/drug problems, defined as present or absent; and physical health, classified as the number of physical problems reported relating to limbs, sight, hearing, skin, chest, heart, stomach, epilepsy, and diabetes. National Opinion Polls interviewers assessed all variables obtained by self-report from study participants. Many of these variables may have changed over time; for example, subjects who were married when they joined the panel might have declared at later waves that they were divorced. These changes were taken into account in the analyses by using the values recorded at the wave being studied.

Statistical methods
The data consisted of five successive waves of observations. Stata statistical software was used (version 5; Stata Corporation, College Station, Texas). For each person, data from waves 2–5 were appended to those for wave 1.

First, the distribution of respondents' characteristics at entry into the panel was examined, and their baseline smoking prevalence was studied. Second, multivariate analyses were performed to study the association between increased cigarette smoking and past mental disorder, including other potential risk factors and confounders. Records for waves 2–5 were analyzed jointly by using logistic regression models. Survival analysis was not indicated because the outcome of interest was whether there was an increase in cigarette smoking after a fixed 12 months, not the amount of time that elapsed before cigarette smoking increased. The results were presented as odds ratios with 95 percent confidence intervals. Likelihood ratio tests were used to evaluate the significance and linear trend of risk factors and interactions between them. The likely correlation between records obtained on the same persons (and on the same households) at different waves was taken into account by computing the 95 percent confidence intervals for the odds ratios with the Huber method (27Go). This method gives results similar to those obtained with generalized estimating equations by using an identity correlation matrix (28Go).

Finally, to examine the quality of the British Household Panel Survey data, we defined an indicator of participation in the last wave, since this wave should have included all panel members. Possible presence of attrition bias was then assessed by comparing the results obtained for the subset of subjects who did or did not participate in the last wave.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A total of 12,057 subjects were recruited between 1991 and 1995, 9,598 in the first wave and 2,459 in subsequent waves as a result of household changes. The distribution of risk factors at the time of recruitment is shown in table 1. Forty-nine percent were men (n=5,847), the mean (standard deviation) age of the cohort was 41.7 (16.2) years, 31.7 percent were smokers, and 25.3 percent were GHQ-12 positive.


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TABLE 1. Distribution of all variables by the wave during which subjects entered the British Household Panel Survey in 1991–1995

 
The results of univariate analyses of the data at recruitment showed that the odds of current smoking were significantly greater for subjects who were young, male, of lower socioeconomic status, and divorced; had physical or alcohol/ drug problems; and were GHQ-12 positive (table 2). A significant linear association was found between number of cigarettes smoked and increasing concurrent GHQ-12 score (correlation coefficient=0.29, p<0.0001).


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TABLE 2. Univariate logistic regression analysis of the current smoking status of all subjects at entry into the British Household Panel Survey in 1991–1995

 
Increased cigarette smoking was recorded as present if the difference in the number of cigarettes smoked between successive waves was five or more. Past mental disorder was derived from the GHQ-12 in waves 1–4 but was used as an exposure for subsequent waves (waves 2–5, respectively). As indicated in nearly 6 percent (n=2,105) of the records for these last four waves, respondents' cigarette smoking had increased over the previous 12 months, and about a third of these subjects (n=751 records) had been nonsmokers in the previous wave. By examining successive records for the same subjects, we found that the probability of increasing cigarette smoking increased if a subject had already increased cigarette smoking before; those whose cigarette smoking had increased between waves 1 and 2 (5.5 percent) were about 20 percent more likely to further increase their cigarette smoking between subsequent waves.

Results obtained from alternative logistic regression models for increased cigarette smoking are shown in table 3. The model 1 column shows the crude odds ratio for increased cigarette smoking by past mental disorder. Adding the demographic variables as indicator variables (age group, sex, and socioeconomic status) significantly improved model 1 but did not modify the association between past mental disorder and increased cigarette smoking. Adding marital status and education (model 2) and the health variables, as well as alcohol/drug problems and physical problems (model 3), showed that each of these variables (with the exception of physical problems) significantly improved the models and was independently associated with increased cigarette smoking, but none confounded the effect of past mental disorder on increased cigarette smoking.


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TABLE 3. Multivariate logistic regression analysis of increased smoking, by past mental disorder and other explanatory variables, for subjects who entered the British Household Panel Survey in 1991–1995

 
Interactions between exposure variables were examined and showed a weak modification of the effect of past mental disorder for different age groups (likelihood ratio test: {chi}2=6.8 (3 df), p=0.078). Stratification by age group showed that the association between past mental disorders and increased cigarette smoking was strongest for the youngest (16–21 years) and oldest (51–75 years) age groups, less significant for the group aged 22–30 years, and not significant for the group aged 31–50 years (table 4). Accounting for the additional clustering of observations because some of the respondents resided in the same households did not change any of the results (not shown).


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TABLE 4. Age-specific adjusted* odds ratios for increased smoking, by past mental disorder, British Household Panel Survey, 1991–1995

 
To examine whether attrition bias affected our results, we repeated the analyses on the two subsets of respondents who were either present (n=8,572) or not present (n=3,485) in wave 5. Although those who did not participate in wave 5 were more likely to be smokers (odds ratio=1.25, 95 percent confidence interval: 1.15, 1.36) and to have had a past mental disorder (odds ratio=1.14, 95 percent confidence interval: 1.03, 1.25), the estimated association between past mental disorder and increased cigarette smoking did not substantially differ between the two subsets (likelihood ratio test: {chi}2=0.2 (1 df), p=0.638).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Cross-sectional data from the first five waves of the British Household Panel Survey showed that at every wave, a quarter of the subjects suffered from a common mental disorder. We found that such persons were about 30 percent more likely to have increased their smoking 12 months later.

The General Health Questionnaire is considered almost a "gold standard" for screening common mental disorders in the general population, as it has substantial reliability and very good sensitivity and specificity when tested against standardized psychiatric interviews (25Go, 29Go). The most common mental disorders in the general population are mixed depressive and anxiety disorders (30Go, 31Go). Anxiety disorders may have diluted the association we found, because depression is more strongly associated with smoking than is anxiety (8Go, 9Go).

To limit recall bias, the GHQ-12 measures recent change in mental health compared with a person's usual state. There may have been some confounding by psychiatric morbidity that might have been present between waves but had remitted at the time of interview. It is difficult to examine for this type of confounding because the majority of mental disorders in the general population appear to remit (32Go), but about 30 percent have a chronic course (33Go). Smokers tend to underreport how much they smoke by 5–10 percent, suggesting that the associations we observed are likely to be dilutions of the true association (34Go).

Substance misuse is strongly associated with smoking (8Go), but the proportion of subjects in this study who reported alcohol and drug problems (0.4 percent) was much lower than the national prevalence of alcohol and drug dependence, 7 and 1 percent, respectively (31Go), suggesting a high misclassification rate in this study. It is possible that this misclassification introduced a bias in the association of past mental disorder with increased smoking. The direction of any misclassification bias is likely to result in underestimation of the association between past mental disorder and increased smoking, as underreporting of alcohol and drug problems is probably correlated with underreporting of cigarette smoking. There is evidence that after adjustment for alcohol problems, the association between common mental disorders and smoking remains (8Go, 9Go), as was found in our study; however, adjustment for a misclassified confounder is not sufficient to remove the effects of bias regarding its measurement.

The most vulnerable age group appears to be young adults; subjects aged 16–21 years (including new smokers) were 50 percent more likely to have increased their cigarette smoking if they had had a mental disorder in the previous year. The upper confidence limit showed an estimated doubling of this risk. Subjects in the oldest age group, 51–75 years, were 60 percent more likely to have increased their smoking. People in this age group are making transitions into retirement, bereavement, and the onset of physical morbidity, and these may be reflected by the GHQ-12. We speculate that the smaller effect of recent common mental disorders and increased smoking in the groups of subjects aged 21–30 and 31–50 years may reflect their greater health consciousness.

The advantage of our study is that we used panel data that combined cross-sectional data into a longitudinal design. Panel designs are especially suitable for studying mental disorders, as the precise date of onset is often difficult to define. We also used a very large sample, so random error was likely to be small. Our associations did not appear to be invalidated by attrition bias.

To the best of our knowledge, this is the first British prospective study demonstrating that common mental disorders (predominantly mixed anxiety and depression) affect the risk of increased smoking a year later. This finding indirectly supports the self-medication hypothesis that nicotine, a stimulant, is used to relieve depressive symptoms (6Go). Other mechanisms may also be operating. For example, a large, female twin study found that the association between smoking and depression could be explained by a common genetic predisposition to both (35Go). A third possibility is that smoking, and other unhealthy lifestyles, can be viewed as a continuum of self-destructiveness, the extreme end of which is suicide (17Go). Depressed persons have low self-esteem, are pessimistic about the future, and lose interest in their health. It has been postulated that nicotine is itself depressogenic (36Go). Although we could not test this theory directly, previous smoking may be associated with increased General Health Questionnaire scores, which then further increases cigarette smoking.

In the general population, psychiatric morbidity is common and treatable. Our study suggests that a subgroup of persons who have significant mental health problems are more likely to increase their cigarette smoking and that this behavior is most likely to occur in the youngest age groups, who are at risk of becoming lifetime smokers. This finding suggests that smokers, especially younger and older adults, may benefit from increased help for their psychological distress, which may improve both their smoking behaviors and, consequently, their physical morbidity and mental health.


    ACKNOWLEDGMENTS
 
Dr. Ismail was supported by a Wellcome Trust Training Fellowship in Clinical Epidemiology (grant AA27) in 1996–1997, when this study was started.

The data used were made available through the Economic and Social Research Council Data Archive. The ESRC Research Centre originally collected the data on microsocial change at the University of Essex, England, United Kingdom. Neither the original collectors of the data nor the Archive bear any responsibility for the analyses or interpretations presented in this paper.

Dr. Ismail was the principal investigator, generated the study hypothesis and design, initiated the study, undertook data management and statistical analysis, and drafted the manuscript. Mr. Sloggett and Dr. De Stavola contributed to the development of the hypothesis, design, and analysis and helped prepare the manuscript.

The authors thank Drs. Jan Neeleman, Mike Farrell, and Nick Buck, who commented on an earlier draft of this manuscript.


    NOTES
 
Correspondence to Dr. Khalida Ismail, Department of Psychological Medicine, Guy's, King's and St. Thomas' School of Medicine, 103 Denmark Hill, London, England, United Kingdom SE5 8AZ (e-mail: khalida.ismail{at}iop.kcl.ac.uk).


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 DISCUSSION
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Received for publication April 20, 1999. Accepted for publication November 1, 1999.