From the Department of Economics, University of Colorado, Denver, CO
Correspondence to Dr. Daniel I. Rees, Department of Economics, University of Colorado, Campus Box 181, Denver, CO 80217-3364 (e-mail: drees{at}carbon.cudenver.edu).
Received for publication October 20, 2004. Accepted for publication December 22, 2004.
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ABSTRACT |
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adolescent; depression; health; smoking
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INTRODUCTION |
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Recently, however, a third explanation for the association between smoking and depression has been proposed, namely, that smoking itself leads to emotional disturbances (1012
). This explanation is supported by a number of influential studies on teenagers and, if true, would have enormous implications both because the treatment and consequences of depression are so costly, and because policymakers would have a potentially powerful tool to reduce the incidence of a widespread illness.
Previous researchers in this area have shown that teens who smoke are at increased risk of subsequently developing the symptoms of depression (10, 11
). This association, however, could be driven by unobserved (from the standpoint of the researcher) factors having to do with the home environment or with an individual's genetic makeup. In order to explore the role played by difficult-to-measure environmental and genetic influences potentially correlated with both smoking and depression, we compare estimates from standard regression models, which can be thought of as providing "naïve" estimates of the effect of smoking on depression, with fixed effects estimates that completely control for time-invariant factors.
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MATERIALS AND METHODS |
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The Adolescent Health Study began with a stratified, random sample of all high schools with more than 30 students in the United States. Eighty high schools were chosen from this population, and an additional 52 middle or "feeder" schools from the same communities were included in the study. Any student who appeared on the roster of one of these 132 schools was eligible to participate in the Adolescent Health wave I (baseline) in-home survey. The wave I in-home interviews were conducted primarily between May and September of 1995 and produced a nationally representative sample of students aged 1121 years in grades 7 through 12. Wave II (follow-up) in-home interviews were conducted between April and August of the following year. The mean period between baseline and follow-up interviews was 10.9 months.
We analyze data from the wave I and wave II in-home surveys. These surveys contain good measures of tobacco use and depressive symptomatology, as well as personal characteristics, family background variables, and a large array of contextual variables that pertain to a respondent's county and state of residence. To address data confidentially and security issues and to minimize the potential for interviewer or parental influence, respondents entered their answers on a laptop computer. For particularly sensitive questions, the respondent listened to prerecorded questions through earphones and then directly entered the answers. Of the 18,924 respondents in the wave I in-home weighed sample, 13,569 were reinterviewed at follow-up. An additional 501 observations were lost because of missing data (table 1).
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A little more than a quarter of the sample (25.3 percent of males and 25.8 percent of females) smoked at baseline. By follow-up, a little over a third of the sample smoked (33.4 percent of males and 33.9 percent of females). These data are consistent with those from other nationally representative surveys of adolescent smoking from the mid-1990s (15). Male smokers at baseline consumed, on average, 9.33 packs per month, while female smokers consumed, on average, 7.74 packs per month.
To assess depressive symptomatology, the Adolescent Health in-home survey included 18 of the 20 items that make up the Center for Epidemiologic Studies Depression (CES-D) Scale (16). For instance, respondents were asked how often during the past week they were "bothered by things that usually don't bother you," how often did they not "feel like eating," and how often did they feel "like you couldn't shake off the blues even with help from your family or your friends?" (The two missing items from the CES-D questionnaire were "my sleep was restless" and "I had crying spells.") Each response was coded on a scale of 03 based on frequency (0 = rarely or none of the time; 1 = some or a little of the time; 2 = occasionally or a moderate amount of the time; and 3 = most or all of the time), and adding up these responses produced a score of between 0 and 54 (Cronbach's
= 0.86). To facilitate the comparison of our results with those of previous researchers, we rescaled this score to correspond with the 20-item CES-D Scale (11
). Thus, respondents could be assigned a CES-D score of between 0 and 60, with higher numbers indicating the presence of more depressive symptoms.
The primary focus of the present study is not on depression per se but on its symptoms as measured by the CES-D Scale. Accordingly, the CES-D score is initially treated as a continuous variable. However, previous work has shown that a dichotomized version of the CES-D Scale can be used as a screening instrument for depression in an adolescent population, provided that appropriate cutpoints are chosen (17). Using the cutpoints of Goodman and Capitman (11
), we present additional analyses in which the CES-D score is dichotomized.
Statistical models
We begin our study by estimating the effect of smoking on depressive symptomatology using a standard linear regression framework in which the CES-D score of individual i at follow-up (t = 2) is related to a set of observable factors and smoking behavior at baseline (t = 1) by the following equation:
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Estimating equation 1 using ordinary least squares will produce unbiased estimates of 1 and
2 if the error term,
i, t = 2, is uncorrelated with smoking behavior. However, if unobservable environmental or genetic factors are correlated with both the CES-D score at follow-up and smoking behavior, then ordinary least squares estimates will be biased. Furthermore, because the baseline CES-D score is not included as an explanatory variable in equation 1, this approach is subject to a problem of reverse causality: That is, if preexisting depression leads to smoking at baseline, then it is inappropriate to interpret ordinary least squares estimates of
1 and
2 as the effect of smoking on depressive symptomatology.
We address these problems by taking fuller advantage of the longitudinal nature of the Adolescent Health data by modifying equation 1 to include individual-specific intercepts, often called "fixed effects" (18). These fixed effects, denoted
i, are incorporated into equation 1 as:
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The fixed effects approach can also be used when the dependent variable is dichotomous. Following previous research, we create a high depressive symptomatology variable that is equal to one for youth scoring above a cutpoint on the CES-D Scale, and zero otherwise (11). The cutpoint for females is 24; for males, the cutpoint is 22. The logistic model is derived by assuming that the probability that respondent i scores above the cutpoint at the follow-up interview takes the following form:
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However, if smoking behavior is correlated with unobservable factors, then the parameter estimates from equation 5 will be biased in the same way that the ordinary least squares estimates are biased. One solution is to once again take advantage of the longitudinal nature of the Adolescent Health data by modifying equation 4 to include individual-specific intercepts. Including individual fixed effects in the logistic model leads to a log odds ratio of the following form:
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RESULTS |
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Table 3 presents ordinary least squares estimates of the effect of smoking on depressive symptoms by gender. As noted above, ordinary least squares estimates are subject to a variety of limitations and are presented for comparison purposes only.
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For females, smoking participation is associated with a 2.92-point increase in the CES-D score, and each additional pack is associated with an increase of 0.06 in the CES-D score. These estimates suggest that a female smoking 7.74 packs per month (the baseline average) would score 3.39 points higher (95 percent CI: 2.65, 4.12; p < 0.001) on the CES-D Scale than would a comparable nonsmoker.
Adding fixed effects to the linear regression model
Table 4 presents estimates of the linear regression model augmented with fixed effects. The results indicate that simple ordinary least squares may substantially overstate the relation between smoking and the symptoms of depression. For males, smoking participation is associated with a 0.66-point increase in the CES-D score, and each additional pack per month is associated with a 0.02 increase in the CES-D score. These estimates suggest that a male smoking 9.33 packs per month (the baseline average) would score 0.84 points higher on the CES-D Scale (95 percent CI: 0.44, 1.23; p < 0.001) than would a comparable nonsmoker, a number that is less than one third the size of the corresponding ordinary least squares estimate. For females, smoking participation is associated with a 1.16-point increase in the CES-D score, and each additional pack per month is associated with a 0.01 increase in the CES-D score. A female smoking 7.74 packs per month (the baseline average) is predicted to score 1.25 points higher (95 percent CI: 0.75, 1.75; p < 0.001) than a comparable female nonsmoker, a number that is approximately one third the size of the corresponding ordinary least squares estimate.
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DISCUSSION |
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If it could be confirmed that smoking is a contributing factor to depression in a causal sense, this would represent an important step toward identifying the physical process that produces depression. Moreover, it could provide useful information to policymakers and health specialists interested in reversing the upswing in youth smoking that occurred during the 1990s. According to data from national surveys such as the Monitoring the Future survey, the incidence of smoking among high school students grew dramatically between 1991 and 1997, although it has been suggested that recent increases in the price of cigarettes may have the effect of reversing this upward trend (15). If increases in the price of cigarettes also have the effect of reducing depression among high school students, this would be welcome news indeed.
Previous research in this area clearly indicates that adolescents who smoke are at higher risk of subsequently developing the symptoms of depression (10, 11
). This association, however, could be driven by underlying influences related to the home environment or an individual's genetic predisposition to depression. Previous researchers have typically examined the predictors of adolescent smoking in order to rule out this possibility. For instance, Goodman and Capitman (11
), using data from the Adolescent Health study, found that teens with CES-D scores above a certain cutpoint were no more likely to start smoking than were teens below the cutpoint. However, Goodman and Capitman included a number of covariates in their model that may indicate depression (e.g., grade point average, alcohol use, previous smoking, measures of delinquency and self-esteem, and parental perceptions of the respondent's behavior). If one is interested in ruling out the possibility that depression affects whether a teen takes up smoking, then it is inappropriate to control for potential indicators of depression.
The results of the present study confirm that smokers tend to exhibit more symptoms of depression than do nonsmokers. Female respondents to the Adolescent Health survey who smoked at baseline scored, on average, 3.31 points higher on the CES-D Scale at follow-up than did their nonsmoking counterparts. Male smokers scored, on average, 3.11 points higher on the CES-D Scale at follow-up than did their nonsmoking counterparts.
Although observable factors, such as personal and parental characteristics, have been shown to be important predictors of adolescent depressive symptoms (2833
), they do not fully explain these differences. In fact, when age, race, household variables, parental education, urban/rural status, and the county-level unemployment rate are controlled for in a linear regression framework, the average female is predicted to score 3.39 points higher on the CES-D Scale than is her nonsmoking counterpart. The average male smoker is predicted to score 2.90 points higher on the CES-D Scale than is his nonsmoking counterpart.
In contrast, controlling for unobservable factors dramatically reduces the estimated effect of smoking. After the addition of fixed effects to the linear regression model, the average female smoker is predicted to score 1.25 points higher on the CES-D Scale than is her nonsmoking counterpart, while the average male smoker is predicted to score 0.84 points higher on the CES-D Scale than is his nonsmoking counterpart. These estimates suggest that smoking has, at most, an extremely modest impact on depressive symptomatology. To put them in perspective, it might be noted that, if a respondent changed his or her answer from "rarely" to "some of the time" on only one of the 18 items that make up the Adolescent Health version of the CES-D Scale, this would result in an increase of 2.2 points in his or her score.
Yet there is reason to believe that even these modest estimates might overstate the effect of smoking on depressive symptoms. If a physiologic link indeed exists between smoking and the subsequent development of depression, then it would be reasonable to expect that evidence of this link would become more pronounced as the number of cigarettes consumed increases. In fact, we find very little evidence that smoking intensity (as measured by packs smoked per month) is related to the depressive symptomatology of smokers. After the addition of fixed effects to the linear regression model, smoking an extra pack of cigarettes per month is associated with a 0.02 increase in male CES-D scores, an estimate that is small relative to the impact of participation and statistically indistinguishable from zero. For females, smoking an extra pack of cigarettes per month is associated with a statistically insignificant 0.01 increase in CES-D scores.
It is difficult to reconcile these estimates with the notion that smoking causes depression, although it is possible that smoking has different effects depending on the level of depressive symptomatology. The linear regression model, with or without fixed effects, produces estimates of the impact of smoking on the mean CES-D score. In contrast, previous researchers have focused on whether respondents scored above a cutpoint set at a value greater than the mean (3, 8
, 11
). For instance, Goodman and Capitman (11
) investigated the impact of smoking on "high depressive symptomatology" with cutpoints set at approximately the 90th percentile of the CES-D distribution.
Using the cutpoints of Goodman and Capitman, we investigated the relation between smoking participation and a dichotomized version of the CES-D Scale. In order to keep our results comparable with those of previous researchers who used logistic modeling, smoking intensity is not included as a predictor of scoring above the cutpoints. This approach is consistent with our finding that smoking intensity is unrelated to the CES-D.
For males, the logistic results are in keeping with those discussed above. That is, controlling for observable characteristics, such as age, race, and parental education, has little impact on the estimated effect of smoking, but adding fixed effects to the standard logistic model produces an estimated odds ratio that is statistically indistinguishable from unity.
For females, we find that logistic modeling produces similar estimates with or without fixed effects. According to the standard logistic model estimates, the odds of scoring above the cutpoint are 2.19 times greater for female smokers than for their nonsmoking counterparts. Adding fixed effects reduces the estimated odds ratio to 1.79.
Given this latter result, we cannot rule out the possibility that smoking has an effect on the likelihood that female adolescents exhibit high levels of depressive symptomatology. However, as noted in Materials and Methods, adding fixed effects produces what is likely to be upper-bound estimates for the effect of smoking. The true effect of smoking on the odds that a female adolescent scored above the cutpoint used by Goodman and Capitman is probably smaller than that reported.
The statistical techniques used in this analysis are designed to produce results comparable with what might be expected from a classically designed, randomized experiment. They are, however, subject to a variety of limitations to which randomized experiments are not. For instance, the inclusion of fixed effects produces unbiased estimates only if the unobservables being controlled for are constant with respect to time. Because unobservables are, by definition, difficult to measure or even characterize, this assumption remains untested.
In addition, including fixed effects does not absolve the researcher from making assumptions with regard to model specification. By exploring the impact of fixed effects in the context of both linear and nonlinear models, we tested the robustness of our estimates to underlying structural and distributional assumptions. Although more work needs to be done in this area, we view our results as evidence of the important role played by unobservable environmental and genetic factors in the determination of adolescent depression. We conclude that, for the average adolescent, the association between smoking and the symptoms of depression can in large part be attributed to the influence of unobservable factors.
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ACKNOWLEDGMENTS |
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References |
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