Changing patterns of cigarette use among white and black youth, US 1976–2003

Changing patterns of cigarette use among white and black youth, US 1976–2003

Social Science Research 36 (2007) 1219–1236 www.elsevier.com/locate/ssresearch Changing patterns of cigarette use among white and black youth, US 197...

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Social Science Research 36 (2007) 1219–1236 www.elsevier.com/locate/ssresearch

Changing patterns of cigarette use among white and black youth, US 1976–2003 夽 Jade Aguilar, Fred Pampel ¤ Population Program, University of Colorado, Boulder, CO 80309-0484, USA Available online 28 September 2006

Abstract To help understand the decline, rise, and then decline again in rates of youth cigarette use over the past several decades, this study examines the changing inXuence of family background and social activities on individual propensities to smoke. Such changes may deWne diVerent trends among youth at high or low risk of smoking and provide insights into the source of the Xuctuations in the trends. After pooling cross-sectional surveys of samples of high school seniors from 1976 to 2003, we test for changes in the association of smoking with family background and social activities using logistic regression models that allow time of survey to interact with individual-level characteristics. The Wndings show little change in the inXuence of individual determinants among black youth, but white youth with highly educated parents and who go out frequently contribute most to the trends in smoking prevalence. © 2006 Elsevier Inc. All rights reserved. Keywords: Smoking trends; Cigarette use; Youth; Family background; Social activities; Sheaf coeYcient



This research was supported by Grant R03CA101498 from the National Cancer Institute. We thank Jason Boardman and anonymous reviewers for comments on an earlier version of the paper. The data come from Jerald G. Bachman, Lloyd D. Johnston, and Patrick M. O’Malley. Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 1976–2003 [Computer Wles], conducted by the University of Michigan Survey Research Center (Ann Arbor, MI: Inter-university Consortium for Political and Social Research [producer and distributor]). * Corresponding author. Fax: +1 303 492 6924. E-mail address: [email protected] (F. Pampel). 0049-089X/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2006.08.002

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1. Trends in youth smoking After falling in the late 1970s and leveling oV at new lows in the 1980s, youth cigarette smoking since the 1990s has received renewed attention because of two unexpected shifts: From 1992 to 1997 smoking rose to a new peak that matched earlier levels in the 1970s, and then fell from that peak during the late 1990s and early 2000s (Crump and Packer, 1998; Department of Health and Human Services (DHHS), 2001: p. 60; Johnston et al., 2005b; Nelson et al., 1995). Thus, 38.8% of high school seniors had smoked daily during the last 30 days in 1976, 27.8% did so in 1992, 36.5% in 1997, and 25.0% in 2004 (Monitoring the Future (MTF), 2004). As shown in Fig. 1, the trends for boys and girls diVer only slightly, with both genders having exhibited the recent rise and decline. The trends diVer more strikingly for white and black youth (see Fig. 2). Smoking among African Americans has fallen more than for whites, and gaps across race groups have grown substantially. In 1977, race and ethnic diVerences for high school seniors were small: Current smoking equaled 38.3% for whites and 36.7% for African-Americans (MTF, 2004). By 2004, the percentages equaled 28.2 for whites and 10.1 for blacks. While smoking among African American fell by 25.6 percentage points, it fell among white youth by only 10.1 percentage points. Like white youth, African American youth showed an increase in smoking in the mid-1990s before declining again in the late 1990s. However, the large gap emphasizes the particularly high rates of smoking among white youth. These often unexpected and puzzling trends do not result simply from the changing composition of the youth population. Given a constant and persistent inXuence of

Proportion Smokers

0.35 0.3

Male Female

0.25 0.2 0.15 0.1 1976 1981 1986 1991 1996 2001 Year

Fig. 1. Proportion smokers by gender and year: MTF data.

Proportion Smokers

0.3 0.2 White Black

0.1 0 1976 1981 1986 1991 1996 2001 Year

Fig. 2. Proportion smokers by race and year: MTF data.

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background risk factors such as parental SES, religious involvement, and academic success on smoking, a changing distribution of these risk factors might produce changes in smoking levels. If groups with risk factors that make them more prone to smoke have sequentially declined, risen, and declined in size, then compositional changes could account for the trends. However, the evidence oVers little support for this possibility (An et al., 1999; Gruber, 2001; Wallace and Bachman, 1991). Controls for background factors do not eliminate the trends and at best explain about 25% of the changes in smoking (Gruber and Zinman, 2001). Rather, the trends appear to have occurred to varying degrees among all groups. Beyond that point, however, it is less clear if some social groups have contributed more than others to the race and gender trends depicted in Figs. 1 and 2. For example, patterns of change may diVer among groups deWned by socioeconomic background. Among adults, smoking and its negative health consequences have become increasingly concentrated among less educated and lower income groups (Escobedo and Peddicord, 1996; Pampel, 2004). Such processes of diVerentiation may also have occurred among youth and produced diVerent trends in smoking among low and high SES groups. Similarly, patterns of change may diVer among groups deWned by their social activities. Those who do less well in school, have fewer ties to religious organizations, and go out often to socialize may diVer from others in their response to the changing social forces that aVect teen smoking. Group diVerences in smoking trends among youth remain largely unstudied, however. Much has been done to describe the (1) gross trends in teen smoking and (2) cross-sectional determinants of teen smoking (see Jacobson et al., 2001 for a review). Yet these two approaches need to be linked to evaluate arguments about the social groups most responsible for the smoking trends. Toward that end, this study builds on a small literature by examining changes in the relationships of family background and social activities with smoking between 1976 and 2003 for white and African-American high school males and females. If some groups of youth have contributed more to the changes than others, it will provide useful information on targeting these groups, help understand the social background factors most responsible for changes in this risky behavior, and give insight into the implications of youth smoking for changing health inequality early in the life course. 2. Explanations Competing explanations of the group sources of changes in teen smoking attend to the behavior of groups at either end of the smoking risk continuum. They diVer on which groups have contributed most to the trends observed for males and females and whites and blacks: Those at high risk, from more disadvantaged backgrounds, and more involved in less conventional social activities or those at low risk, from more advantaged backgrounds, and involved in more conventional social activities. 2.1. High-risk groups On average, high school youth who smoke tend to come from lower SES backgrounds (Droomers et al., 2005; Glendinning et al., 1994; Griesbach et al., 2003; Jacobson et al., 2001, p. 93; JeVeries et al., 2004; Soteriades and DiFranza, 2003). This matches the inverse relationship found among adults between socioeconomic status (SES) and smoking in which those with low education, occupational prestige, and income smoke more than those

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with higher status. In addition, high school youth who smoke tend to have limited academic success and few aspirations for college, go out many nights each week for fun and recreation, show weak religious commitment, and have paid jobs outside of school (An et al., 1999; Bachman et al., 1997, 2002; Bryant et al., 2004; Cronk and Sarvela, 1997; Emmons et al., 1998; Glendinning et al., 1994; Safron et al., 2001; Wallace et al., 1999). If youth from low SES family backgrounds and with pro-smoking social activities are most vulnerable to starting to smoke, they may also be most responsible for the Xuctuations in smoking over the past several decades. Compared to others, they may respond more directly to social forces that aVect smoking, such as changes in cigarette prices, public policies, adult smoking, and pro-smoking or anti-smoking advertising. They would therefore contribute disproportionately to both the fall and the rise in youth smoking. Such changes in the relationships of background and activities with smoking would modify the overall trends. When smoking among high-risk groups declines and brings them closer to the rates of smoking of low-risk groups, youth smoking overall will decline; conversely, when smoking among high-risk groups rises and increases diVerences between groups, youth smoking overall will increase. In Wnding that declines in smoking up to the 1990s occurred more among high-risk students than low-risk students, An et al. (1999) provide support for this argument. They Wnd that smoking changed more for those with poor academic performance, high truancy, lack of ties to religion, and frequent socializing at night. The underlying theoretical premise behind this hypothesis posits the importance of social strain and lack of resources to youth smoking. Among teens (and also adults), lower SES groups face greater social strain than others because of the diYculties caused by the absolute or relative deprivation they face in daily life. Cigarettes and the short-term pleasure provided by nicotine help to moderate this social strain (Colby et al., 1994; Graham, 1995; Johnson and HoVman, 2000). Similarly, teens less committed to school, religion, or other conventional activities may have fewer resources to help them resist pressure from others to smoke, discount attractive advertising images of smokers, and overcome the diYculties of quitting smoking (Mirowsky and Ross, 2003). As a result, these high-risk teens may be more vulnerable to broad social changes that inXuence smoking trends. If so, they will respond more to external inXuences that promote or inhibit smoking and will contribute more to the Xuctuating trends in smoking. 2.2. Low-risk groups Alternatively, changes in smoking among low-risk youth from advantaged backgrounds and involved in anti-smoking social activities may contribute most to the trends. Because high-risk groups already face strong pressures to smoke, social changes may do little to modify those pressures or reduce their high levels of smoking. In contrast, low-risk groups may be most inXuenced by youth cultural trends and changes in peer-group norms that make smoking more or less attractive (Gruber and Zinman, 2001). In empirical terms, the argument predicts much like the last one that the relationships of background and lifestyle factors with smoking will change over time so as to modify the trends. However, the processes involve movement of low-risk groups toward the higher smoking of high-risk groups (i.e., narrowing diVerences) during periods of rising youth smoking, and movement of low-risk groups away from the higher smoking of high-risk groups (i.e., widening diVerences) during period of falling youth smoking.

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The underlying mechanism behind the argument relates less to strain and resources than to group values and fashions. Image and identity play an important role in use of cigarettes among youth, and these factors change as groups attempt to distinguish themselves from others by adopting new behaviors and values (DHHS, 1994, pp. 102–104; Johnson and HoVman, 2000). Low-risk groups on average will smoke less than high-risk groups but nonetheless may be attracted to smoking. They may want to imitate the popular images of smokers in advertising and movies, take part in a mild form of rebellious or unconventional behavior, or enjoy the stimulation of nicotine. It follows that, if low-risk teens are inXuenced to smoke by shifts in youth culture and by changes in external forces of prices, advertising, and larger societal norms, they should exhibit more Xuctuation in their smoking and contribute more to the overtime trends. Several studies oVer support for these arguments. Gilpin et al. (2005) compare smoking patterns among California youth when smoking was rising in the early 1990s and when it was declining in the late 1990s. They conclude that the turnaround resulted primarily from adolescents at low risk for smoking. Using national data on high school seniors, Johnston et al. (2005a) show relatively large changes in smoking among teens with highly educated parents. They similarly suggest that the rise in smoking through the 1990s likely came from the attraction of teens with highly educated parents to the Joe Camel advertising campaign. Also using data on 12th graders, Brown et al. (2001) Wnd that parental education shifted in its association with smoking. In some years, the gap between youth with low and high-educated parents increased signiWcantly, while in other years it fell to near zero. 3. Hypotheses Arguments about which family-background and social-activity groups contribute most to trends in youth smoking and are most vulnerable to forces of change translate into competing predictions. First, if high-risk groups are most aVected by change, they will show the most Xuctuation, moving closer to low-risk groups during downtrends and diverging from low-risk groups during upturns. Second, if low-risk groups are most aVected by change, they will show the most Xuctuation, moving farther from high-risk groups during downtrends and converging toward high-risk groups during upturns. In contrast to these hypotheses, a null hypothesis suggests more simply that changes in smoking will occur similarly across all groups. A thorough test of these hypotheses can improve on existing studies by making sense of contrasting Wndings on the importance of high-risk (An et al., 1999) and low-risk groups (Gilpin et al., 2005; Johnston et al., 2005a,b; Brown et al., 2001) to changes in youth smoking. The hypotheses may Wt some race or gender groups more than others. Since smoking among African Americans has fallen more than smoking among whites (Fig. 2), the processes underlying the changes may diVer as well. Tests therefore need to be done separately for the race and gender groups as well as for all respondents combined. Changes in smoking among low- and high-risk groups may vary across race-gender groups. 4. Methods 4.1. Data We use data from the Monitoring the Future (MTF) Project, which has surveyed nationally representative samples of high school seniors during the spring of each year

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since 1976. Focused speciWcally on youth, the surveys not only ask questions about smoking but also about many background and lifestyle characteristics related to smoking. Other surveys of tobacco use among respondents of all ages seldom ask about family background, school performance, and social activities that relate to smoking decisions. To obtain a nationally representative sample of 12th-grade students, the MTF surveys use multi-stage sampling procedures for the 48 coterminous states. Each year the project Wrst selects geographic areas, roughly 130 schools within the geographical areas, and about 400 students in each school (or the entire senior class if it has fewer than 400 students). From 65% to 80% of the schools have agreed to participate over the years and allowed the students to complete a self-administered questionnaire during a normal class period. Within schools, the response rate of 83% largely excludes those students absent on the day of collection (only about 1% refused to complete the questionnaire). An et al. (1999, p. 700) summarize the conclusions of a good deal of analysis of possible biases in the samples: “While both school and student response rates have varied somewhat over time, adjustments for these diVerences indicate that any bias in overall prevalence rates is likely to be quite small and that variation in response rates over time are not a signiWcant factor in explaining trends in cigarette use.” Combining the white and African-American respondents for each of the surveys from 1976 to 2003 and eliminating those with missing data on the variables yields 269,250 cases. Given privacy concerns, the public-access data do not distinguish Mexican Americans, Puerto Ricans, other Latin Americans, Asian Americans, and Native Americans, which limits our analysis to whites and blacks. The large sample size allows for reliable estimates of changes over time within race and gender subgroups. The survey of high school students during the last semester of their senior year proves well-suited for the study of youth smoking. By some estimates, 88% of those who have ever smoked a cigarette did so for the Wrst time by age 18 (DHHS, 1994, p. 67). The study of high school seniors thus captures a crucial period of decision making. Longitudinal analyses demonstrate high stability or continuity in cigarette smoking during the transition from youth to young adulthood, suggesting further that rates are largely established in high school (Bachman et al., 1997, p. 51; Chen and Kandel, 1995). Given its addictive nature, initiation during youth, and continuation into adulthood, cigarette use is cohort based (Escobedo and Peddicord, 1996; National Cancer Institute, 2001, p. 11), and yearly surveys of high school seniors capture the early smoking experiences of successive cohorts. A problem with the surveys of high school seniors is that they exclude absentees at the time of the survey (about 17% of the sample) and high school dropouts (about 15% of youth at high school ages). Analyses of the MTF data address the possible bias created by these missing groups (Johnston et al., 2005b, pp. 461–472). Concerning the absentees, comparing survey respondents by the number of absences during the last four weeks indicates that those with many absences use substances and drugs more than those with few, but their absence depresses the estimates of smoking prevalence only slightly. More importantly, the underestimate due to absentees remains stable over time and does not inXuence trend results. Concerning dropouts, those who fail to complete high school likely have higher rates of tobacco use than those who graduate. Importantly, however, the percentage of high school dropouts has stayed constant over time (Johnston et al., 2005b, p. 463), which suggests that the higher smoking of dropouts does not aVect the trend and the trend for seniors would not deviate from the trend for the entire class cohort. Further support for this claim comes from studies of youth smoking based on household rather high school

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surveys. Despite including all youth, both dropouts and students, data from the National Health Interview Survey and the National Household Survey on Drug Abuse reveal much the same trends as the MTF surveys (Nelson et al., 1995). 4.2. Measures Over the 28-year time span from 1976 to 2003, the MTF surveys have employed a set of core questions that all respondents answer. The core questions include basic background, lifestyle, smoking, and drug- use variables and provide for consistent time series over the full period. A pooled sample for all years has been created with the following measures. Smoking is measured by questions that ask about ever using cigarettes and about use in the last 30 days. To capture current usage rather than experimentation with cigarettes in the past, we examine the respondents’ daily use over the last 30 days. As done in other studies (e.g., An et al., 1999), we then divide respondents into two categories, those who smoked at least one cigarette a day over the last month and those who did not. Compared to never smokers or occasional smokers, daily smokers have a high likelihood of continuing to smoke as adults (DHHS, 1994) and are the key focus of the dependent variable. Self report questions about cigarette use appear to be reliable and valid (Bachman et al., 1991). Studies of carbon monoxide in blood and cotinine (a nicotine metabolite) in saliva Wnd that self reports are largely valid (Patrick et al., 1994), particularly when the surveys are completed in school, as is the case for the MTF data, rather than at home (Nelson et al., 1995). Not surprisingly, youth are less forthcoming about substance use in household surveys where their parents are nearby than in school where they have privacy from family members. Moreover, self-reports appear to be valid assessments of race and ethnic diVerences in smoking (Wallace et al., 1995, p. 66). One report (DHHS, 1998, 34) notes, for example, that “no evidence indicates that the misclassiWcation bias [of self-reported smoking] explains the substantial decline in smoking prevalence reported by African-American youths.” Besides demographic variables such as sex and race, the MTF data contain a variety of individual background and activity measures relevant to smoking. We discuss these measures brieXy, noting that they are available for all years and have been used regularly in previous studies of smoking among youth (the Appendix lists descriptive statistics for these variables). Parents’ education measures the years of schooling completed by the respondent’s mother or father if available for only one parent and an average score when data is available for both parents. Living arrangements are measured with a dummy variable that codes respondents living with both parents as zero and all others as one. Community size consists of Wve categories ranging from rural/farm communities to urban city centers. Religiosity is measured by combining religious attendance (never, rarely, once or twice a month, and once a week or more) and the importance of religion (not important, a little important, pretty important, and very important) into a single standardized scale (Chronbach’s  equals 0.736). School commitment consists of a standardized scale (Chronbach’s  equals 0.670) that combines four items: self-assessed school ability compared to others the same age (ranging in seven categories from far below average to far above average), selfassessed intelligence compared to others the same age (using the same seven categories), days missed from cutting classes, and grade point average (for categories of A, A¡, B+ and so on to D). Teen’s income is measured by a question on the earnings per week (in tens of real dollars) from a job or other cash source. Social activities are measured by a question

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about how many evenings the respondent goes out during a typical week for fun and recreation (ranging in six categories from less than one to six or seven).1 4.3. Analysis The analysis uses logistic regression to estimate the association of the independent variables with the logged odds of the binary smoking dependent variable.2 Along with the coeYcients, the tables report the model chi-square values and, as a rough guide to explanatory power, a pseudo-R2 based on the proportional change in the model log-likelihood values. We focus most attention, however, on the direction, size, and signiWcance of the coeYcients. Of special importance are the coeYcients for the interactions terms between the independent variables and year of survey. SigniWcant interactions will demonstrate changes over time in the size and direction of the eVects of the determinants and identify the groups that have changed most in their propensity to smoke. Models done separately for race and gender groups supplement the general model for all groups combined.3 To capture the trends over time in smoking and how they may vary across groups, the year of survey can deWne a set of 27 dummy variables for use in the logistic regression models. However, with the background and activity determinants interacting with year, the use of so many the dummy variables makes the models unwieldy. The alternative of using linear, quadratic, or cubic terms for year fails to reXect fully the complexity in the trends depicted in Figs. 1 and 2. To balance complexity, and simplicity, we capture the trend in youth smoking with a sheaf coeYcient, which summarizes the year-to-year changes in smoking with a single term and allows for interactions with the single term. The sheaf coeYcient, Wrst described by Heise (1972) and later treated by Yamaguchi (2002) as the eVect of a parametrically weighted explanatory variable, allows for a single variable and coeYcient to summarize the eVects of a set of variables and coeYcients. The sheaf coeYcient has been used to compare the inXuence of one set of variables with another set. In this case, a new variable based on year of survey is recoded to take the values of the coeYcients that come from a logistic regression of smoking on each of the

1 The positive association of this measure with smoking suggests that fun and recreation involve socializing and partying rather than sports or other activities known to inhibit smoking. 2 The estimation procedure uses listwise deletion of missing data, which reduces the number of cases by 26%. Many students did not answer all questions, and California decided in 1997 to exclude questions on religion. To address this problem, we estimated models on the full sample of cases with valid data on race, gender, and smoking. For each of the other variables, we created a dummy variable indicating valid data and then assigned cases with missing data a score of zero on the original variable. With both variables included in the models, the dummy variables show the diVerence in the logged odds of smoking for cases with valid and missing data, and the original variable shows the eVects for cases with valid data. The respondents with valid data on the independent variables tend to smoke less and change more over time than those with missing data. More importantly, controlling for the level and change in smoking among missing cases does little to change the relationships reported in the tables for cases with non-missing data. 3 In addition to using weights to make the within-year samples representative of the population for each gender-race group, we computed and applied cross-year weights to make the samples sizes of each year equal in all the analyses. Note, however, that the within-year sample weights computed for each school are, in the public use data Wles, collapsed into six values to protect the conWdentiality of the respondents. That means the weighted sample closely approximates the population but does not fully capture the sample design. For example, percentages generated with the full sample are estimated by the MTF project to diVer by less than 1% from those generated with the collapsed weights.

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dummy year variables. For example, in one such logistic regression, those surveyed in 2003 have a coeYcient of ¡.671 (the lowest value relative to the intercept or omitted category of 1976), and those surveyed in 1997 have a high value of ¡.090. The single variable takes these coeYcient values and thereby combines multiple dummy year variables in a way that allows them to be treated as if they were one variable, while still reXecting the size and signiWcance of the larger set of variables (Whitt, 1986, p. 174). In short, the sheaf coeYcient transforms the eVects of multiple variables into a single variable using parametric coeYcients as weights. As shown below, the information in the single variable can, in turn, be unpacked to present yearly changes in predicted smoking. This approach diVers from previous studies. Johnston et al. (2005a,b) examine changes in smoking by education of parents without controls for other variables. An et al. (1999) combine risk factors into a three-category scale and model trends with a polynomial for the three groups of this one variable. Brown et al. (2001) compare changes in the signiWcance of variables over time but not diVerences in the size of signiWcant coeYcients. In contrast, the sheaf coeYcient (1) uses a precisely measured trend variable that reXects yearly changes, (2) examines the size as well as the signiWcance of group diVerences in smoking trends with multivariate controls, and (3) considers diVerences in smoking trends for groups deWned by each of the individual risk factors. 5. Results The logistic regression models in Table 1 summarize the trends and patterns in smoking for all race and sex groups combined. The Wrst model in the table merely demonstrates that the sheaf coeYcient for year reaches signiWcance (i.e., levels of smoking change signiWcantly over time). The coeYcient of 1.0 summarizes the decline, increase, and decline of youth smoking from in the late 1970s to the early 2000s and provides a base for comparisons with controls. The small pseudo-R2 of 0.7% means that nearly all of the pseudo-variance occurs within rather than across years. Still, the changes involve more than random Xuctuation and, as shown in equations to follow, remain even with controls for individual determinants. The cross-sectional relationships between smoking and youth background appear in model 2. On average, black youth smoke less than white youth (b D ¡1.328), and girls smoke slightly more than boys (b D 0.109). Parents’ education level decreases (b D ¡0.079) and not living with both parents increases the likelihood of the teen smoking (b D 0.422). Those living in larger communities smoke more than those from smaller communities and rural areas (b D 0.023)—a result that will, however, diVer across races and often prove insigniWcant with other controls. Current activities inXuence smoking more strongly than family background. In Model 3, adding the activities variables reveals that (1) the stronger the academic commitment, the lower the smoking (b D ¡0.525); (2) the more money received from a job or other sources, the higher the smoking (b D 0.065); (3) the stronger the religiosity, the lower the smoking (b D ¡0.337); and (4) the more days spent going out with friends each week, the higher the smoking (b D 0.315). With the addition of the activity variables to the model, the pseudo-R2 rises from 0.032 to 0.130 and the coeYcients for parents’ education, living arrangements, and community size decline in size. Note that the sheaf coeYcient declines by only 2.3% with controls for composition.

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Table 1 Logistic regression coeYcients for models of youth smoking: All respondentsa Independent variables

Model 1.1

Model 1.2

Model 1.3

Model 1.4

Year sheafb Race—Black Sex—Female Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out Parents’ education £ Year sheaf Frequency goes out £ Year sheaf Constant

1.000***

. 987*** ¡1.328*** .109*** ¡.079*** .422*** .023***

.977*** ¡1.153*** .383*** ¡.025*** .258*** ¡.013* ¡.525*** .065*** ¡.337*** .315***

¡.956

¡.002

¡1.140

¡.027 ¡1.153*** .383*** ¡.008 .258*** ¡.013* ¡.525*** .065*** ¡.337*** .354*** .047*** .101*** ¡1.783

Model  Pseudo-R2

1382 .0069

5741 .0322

20,704 .1302

20,769 .1304

2

Source: Monitoring the Future 12th graders 1976–2003, N D 269,250. * p < .05, **p < .01, ***p < .001, two-tailed test. a Estimates weighted for sample design with STATA 8.0. b 1976 D 0 1977 D ¡.030 1978 D ¡.095 1979 D ¡.176 1980 D ¡.423 1981 D ¡.493 1982 D ¡.470 1983 D ¡.474 1984 D ¡.616 1985 D ¡.578 1986 D ¡.583 1987 D ¡.621 1988 D ¡.610 1989 D ¡.560 1990 D ¡.560 1991 D ¡.584 1992 D ¡.714 1993 D ¡.504 1994 D ¡.420 1995 D ¡.291 1996 D ¡.286 1997 D ¡.090 1998 D ¡.183 1999 D ¡.187 2000 D ¡.343 2001 D ¡.430 2002 D ¡.490 2003 D ¡.671.

The last column adds interaction terms to test for diVerences across groups in the trends. Product terms of the year sheaf coeYcient by each of the determinants were tested, but two emerged most strongly and consistently—one involving year by parents’ education and one involving year by frequency of going out. Given the large sample size, signiWcance tests alone do not provide evidence of meaningfulness, and the crude pseudo-R2 measure increases little with the product terms. For several reasons, we do not attribute much importance to the R2 result. The meaning of variance in logistic regression is ambiguous and any one of the many pseudo-R2 measures oVers only a rough approximation of explanatory power. For testing time-based interaction models, the limits are more serious because within-year diVerences in smoking swamp between-year diVerences (shown by the low explanatory power of model 1). As a result, the additive terms for variables that vary within years will contribute vastly more to the pseudo-R2 than the cross-year interactions, and variables relating to year can do little to raise the pseudo-R2. Rather than focusing on this statistic, we will oVer other means of making substantive sense of the interaction coeYcients. The sign for the parents’ education interaction is positive (b D 0.047). This indicates that youth with highly educated parents and from more advantaged backgrounds show greater changes over time in smoking than other youth. Highly educated parents on average reduce smoking of their children, but these children exhibit disproportionately greater changes in smoking over time. The sign for the going-out interaction is also positive (b D 0.101). This indicates that youth who go out often show greater changes over time in smoking than other youth. On average these youth smoke more than others, but they also exhibit disproportionately greater changes in smoking over time. Thus, one group of

Proportion Smokers

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0.22 Low 0.18

Med. High

0.14 0.1 1976 1981 1986 1991 1996 2001 Year

Proportion Smokers

Fig. 3. Predicted proportion smokers by parents’ education and year.

0.38 0.3

Low

0.22

Med. High

0.14 0.06 1976 1981 1986 1991 1996 2001 Year

Fig. 4. Predicted proportion smokers by frequency goes out and year.

low-risk smokers (with highly educated parents) and another group of high-risk smokers (who go out often) respond most intensely to social changes.4 To present these results visually, the coeYcients from the interaction models are used to graph the predicted trends over time for groups deWned by parents’ education and frequency of going out. This is done by assigning the mean to all other variables and selecting three values for parents’ education (the minimum value of six years, the median of 13, and the maximum value of 18 years) and three values for the frequency of going out (the minimum value of one, the midpoint value of 3.5, and the maximum value of six). For each of these three values, the predicted logged odds of smoking in each of the years is computed from the coeYcients for year, parents’ education or frequency of going out, and the interactions. The predicted logged odds are then transformed into probabilities and graphed in Figs. 3 and 4. Fig. 3 shows that the smoking of youth with highly educated parents Xuctuates more widely than youth with less educated parents.5 In the late 1970s, the groups have similar levels of smoking, but more advantaged youth show the greatest decline during the early 4

With controls for the missing data variables, the coeYcients for the year sheaf by parents’ education and by going out equal, respectively, .038 (z D 3.93) and .097 (z D 5.36). 5 To better illustrate the diVerences across groups, Fig. 3 uses a narrower band of values on the Y-axis than the other graphs.

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Proportion Smokers

0.44 Low Educ Low Go Out

0.34

Low Educ High Go Out

0.24

Med Educ Med Go Out High Educ High Go Out

0.14

High Educ Low Go Out

0.04 1976 1981 1986 1991 1996 2001 Year

Fig. 5. Predicted proportion smokers by parents’ education, frequency goes out, and year.

1980s. Further, when smoking starts to increase in the early 1990s, youth from more highly educated backgrounds increase their smoking the most and nearly reach parity with the smoking level of youth from less educated backgrounds. When smoking starts to fall in the late 1990s, these youth again respond most to the forces behind the trends. As illustrated by the graph, the diVerence in predicted smoking between youth with values on parents’ education of 6 and 18 ranges widely. The gap begins at 1.7% in 1976, reaches to 5.8% in 1992, returns to 2.4% in 1997, and then goes to 5.7% in 2003. Assuming that these groups changed identically misses a change in the gap of more than 4%. Fig. 4 shows that the smoking of youth who say they go out frequently Xuctuates more than for other youth. Along with smoking considerably more than others, these youth exhibit a greater decline in the 1980s, rise in the early 1990s, and fall thereafter in smoking. In contrast, those who go out rarely change little over the time span. The gap between the two groups thus ranges from a low of 16.1% in 1992 to a high of 30.6% in 1976. With the gap varying by up to 14.1%, the model and the graphs illustrate the substantive importance of the interaction coeYcients and of the diVerent trends across groups. Calculations that combine the results for parents’ education and going out can further highlight the diVerent trends among high school seniors (see Fig. 5). The group with high parents’ education and frequently going out changes the most, while the group with low parents’ education and rarely going out changes the least. These two groups are neither the highest nor the lowest smoking groups, but they are the ones that respond most and least to the changing social forces that promote or inhibit smoking. The gap between the two groups falls from 29.2% in 1976 to 10.8% in 1992, rises again to 26.5% in 1997, and Wnally falls again to 11.7% in 2003. These comparisons thus produce a gap with a range of 18.4%. Although these changes may not explain a large proportion of the overall variation, they contribute importantly to understanding the trends in smoking. Table 2 replicates the models in Table 1 separately for white males and females. Given that whites make up the large majority of the sample, the eVects of the determinants are similar to those for all groups combined and similar for both boys and girls. Both interactions remain signiWcant (b D 0.094 and 0.092 for males and 0.073 and 0.134 for females). The interaction for parents’ education is particularly strong among white males; smoking of white males Xuctuates even more than for all races and sexes combined. The results for black youth diVer from those for whites, however. In Table 3, neither parents’ education nor frequency of going out interact signiWcantly with year (b D ¡0.038

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1231

Table 2 Logistic regression coeYcients for models of youth smoking: White respondentsa Independent variables White males Year sheaf Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out Parents’ education £ Year Sheaf Frequency goes out £ Year Sheaf Constant Model 2 Pseudo-R2 White females Year sheaf Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out Parents’ education £ Year Sheaf Frequency goes out £ Year Sheaf Constant Model  Pseudo-R2 2

Model 1.1

Model 1.2

Model 1.3

Model 1.4

1.000***

1.026*** ¡.083*** .386*** ¡.021**

.966*** ¡.017*** .220*** ¡.035*** ¡.505*** .064*** ¡.257*** .298***

¡1.011

.095

¡1.651

¡.665* .013 .222*** ¡.035*** ¡.507*** .065*** ¡.257*** .326*** .094*** .092** ¡2.158

751 .0092

1649 .0202

7789 .1123

7826 .1128

1.000***

.968*** ¡.087*** .476*** .082***

1.076*** ¡.034*** .315*** .025** ¡.581*** .076*** ¡.401*** .343***

¡.874

¡.042

¡1.053

¡.405 ¡.013 .315*** .025** ¡.581*** .076*** ¡.401*** .384*** .073*** .134** ¡1.489

559 .0059

1956 .0206

10869 .1375

10886 .1378

Source: Monitoring the Future 12th graders 1976–2003, N D 113,161 (white males) and N D 121,238 (white females). * p < .05, **p < .01, ***p < .001, two-tailed test. a Estimates weighted for sample design with STATA 8.0.

and 0.019 for males and 0.012 and ¡0.043 for females). These insigniWcant interaction results reveal little change over time in the inXuence of any of the social background or activity variables. The determinants have the expected additive inXuence on smoking among black youth—the eVects for black teen smokers are similar to those for white teen smokers. Unlike whites, however, the generally downward trend in smoking among black youth occurs similarly among subgroups. 6. Conclusion The analysis of survey data on smoking of high school seniors from 1976 to 2003 is consistent with previous studies showing that the trends in youth smoking do not result simply from the changing composition of the population. Rather, changes over time in smoking with controls for individual-level variables available from the surveys indicate something more: To varying degrees, trends in smoking aVect youth from all social backgrounds and involved

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Table 3 Logistic regression coeYcients for models of youth smoking: Black respondentsa Independent variables Black males Year sheaf Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out Parents’ education £ Year sheaf Frequency goes out £ Year sheaf Constant Model 2 Pseudo-R2 Black females Year sheaf Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out Parents’ education £ Year sheaf Frequency goes out £ Year sheaf Constant Model  Pseudo-R2 2

Model 1.1

Model 1.2

Model 1.3

Model 1.4

1.000***

.971*** ¡.023 .283*** ¡.090**

.917*** ¡.009 .201** ¡.119*** ¡.268*** .040*** ¡.286*** .184***

¡1.031

¡.663

¡.929

1.310*** ¡.044 .205** ¡.118*** ¡.268*** .040*** ¡.288*** .201*** ¡.038 .019 ¡.584

326 .0503

362 .0559

540 .0880

550 .0884

1.000***

1.041*** .013 .335*** .029

1.050*** .025* .213** ¡.036 ¡.350*** .034*** ¡.445*** .240***

¡1.103

¡1.481

¡.973

1.045*** .037 .210** ¡.036 ¡.350*** .034*** ¡.443*** .195*** .012 ¡.043 ¡.967

659 .0855

687 .0894

1070 .1384

1047 .1387

Source: Monitoring the Future 12th graders 1976–2003, N D 14,417 (black males) and N D 20,434 (black females). * p < .05, **p < .01, ***p < .001, two-tailed test. a Estimates weighted for sample design with STATA 8.0.

in a diverse variety of activities. Additionally, however, the trends for whites are disproportionately driven by two types of teens: Those with highly educated parents and those who frequently go out. The same pattern does not occur among black youth, where no interactions with year are apparent. The downward trend occurs similarly among all black youth. The results imply that white youth with highly educated parents and who frequently go out have been more inXuenced than others by changes that make smoking more or less attractive. Their smoking declined more than for others during the late 1970s and early 1980s, rose more in the early to mid-1990s, and declined more in the late 1990s and early 2000s. Perhaps surprisingly, one group (with highly educated parents) smokes less than average, while the other group (which goes out often) smokes more than average. However, both groups have special importance in driving trends in youth smoking. The results for whites are partly consistent with the low-risk hypothesis and the results presented by Gilpin et al. (2005). They conclude from an analysis of youth in California that “The decline in transition to any smoking between cohorts occurred primarily among

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1233

adolescents at lowest risk of future smoking, although small, insigniWcant declines were present in the medium- and high-risk groups.” Our results for parental education indicate much the same (as do the results of Johnston et al., 2005a,b). At the same time, the results are partly consistent with the high-risk hypothesis and the results reported by An et al. (1999). They conclude, “Between 1976 and 1990, greater absolute declines in smoking occurred among high-risk students (17 percentage points) than among low-risk students (6 percentage points).” Our results for frequency of going out conWrm these results. In addition, our Wndings go beyond these studies by also linking ostensibly competing Wndings about low- and high-risk youth. The similarity between low-risk teens with highly educated parents and high-risk teens who socialize often suggests some original insights. Together, these two characteristics deWne smokers not mentioned by previous studies but who may deserve attention as innovators with respect to smoking trends. White youth who are both from more advantaged backgrounds and highly sociable may serve as leaders in social tastes and popular activities. Youth culture, identity, and peer inXuence play a crucial role in teen smoking but change in unexpected and puzzling ways. Attention to teens whose smoking changes the most and appears to drive larger trends can give some insight into these changes. Identifying those most responsive to changes in smoking can also help public health programs to target their anti-smoking eVorts. Since changes have occurred among all youth groups, policies and programs to reduce smoking must appeal to a diverse audience (and national anti-smoking campaigns appear to do that). In addition, however, the groups identiWed in the analysis may warrant special attention. As leaders in the rise of smoking in the early 1990s and the more recent decline, white youth with more educated parents and more likely to go out at night can play a crucial role in preventing future increases in smoking. However, processes at work for blacks appear to diVer from those for whites. Since the trends have occurred similarly among all groups, it is harder to single out trend leaders. Our results face several limitations. To maintain compatibility across surveys over a long time span, other possible determinants could not be included in the models. Measures of the smoking status of parents and teen peers would no doubt do much to account for patterns of smoking among youth (although they would not account for why the smoking of parents and friends changes). Measures involving concern about weight, exposure to cigarette marketing eVorts, and the experience of media or school anti-smoking campaigns would likely prove important as well. The sample would be improved if high school dropouts could be included. In addition, the results are limited by the use of cross-sectional data for 12th graders. On one hand, the cross-sectional results for 12th graders may not reXect relationships at younger ages. For example, if students who started smoking in 9th grade when they were less studious later came to concentrate more on school in 12th grade but continued to smoke, it would reduce the otherwise negative relationship between academic performance and smoking. On the other hand, the cross-sectional results for 12th graders will not reXect smoking that begins at older ages. Because a greater percentage of black males start smoking after age 18 than white males (DHHS, 1998, p. 41), the results for 12th graders will not capture the determinants of these late starters and may present a misleading picture of racial trends in smoking. Although these limitations in the cross-sectional data will attenuate true relationships, they do not eliminate them altogether. Since the vast majority of smokers start before 12th grade, it limits the extent of the error from studying only seniors and allows for meaningful analyses. As a result, strong and robust relationships between

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family background and social activities of 12th graders have been identiWed here as well as elsewhere. We cannot trace out the life-course pathways that lead to adult patterns of smoking but can address the more modest goal of describing the uneven changes in smoking over the last several decades. Other limitations include a lack of focus on the macro-level forces that transcend and shape the inXuence of individual traits on youth behavior. Changes in youth values toward health risks and deviance (DeCicca et al., 2002), social pressures faced by youth (Johnson and HoVman, 2000), advertising and promotional activities of the tobacco industry (Gilpin et al., 1997), counter-advertising (Farrelly et al., 2005), and taxes on and prices of cigarettes (Ross and Chaloupka, 2003) may prove important for the trends in youth smoking. Additional research that complements the individual-level, cross-sectional focus of this study will need to consider how these macro-level factors aVect teen smoking overall and the smoking of trend leaders. Appendix A Descriptive statistics for MTF variables by race and sex Variables

Full sample White male White female Black male Black female Full sample mean mean mean mean Mean SD Minimum Maximum

Smoking Race—Black Sex—Female Parents’ education Lives with one parent or other adult Size of community Academic commitment Own income Religiosity Frequency goes out

.21 .12 .53 13.5 .24

.41 .21 .33 .50 2.40 13.7 .43 .20

.23

.11

.08

13.5 .21

12.8 .51

2.58 .10 4.63 2.77 3.56

1.24 .97 3.48 .93 1.31

2.50 .16 4.25 2.81 3.52

3.06 ¡.23 4.93 3.00 3.59

2.52 .10 5.00 2.62 3.71

12.5 .53

0 0 0 6 0

1 1 1 18 1

3.04 ¡.05 4.50 3.21 2.94

1 ¡4.8 0 1 1

5 2.56 22.1 4 6

N D 269,250.

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