Life course transitions and racial and ethnic differences in smoking prevalence

Accepted Manuscript Title: Life Course Transitions And Racial And Ethnic Differences In Smoking Prevalence Author: Elizabeth M. Lawrence Fred C. Pampel Stefanie Mollborn PII: DOI: Reference:

S1040-2608(14)00010-0 http://dx.doi.org/doi:10.1016/j.alcr.2014.03.002 ALCR 117

To appear in: Received date: Revised date: Accepted date:

19-7-2013 24-3-2014 25-3-2014

Please cite this article as: Lawrence, E. M., Pampel, F. C., & Mollborn, S.,LIFE COURSE TRANSITIONS AND RACIAL AND ETHNIC DIFFERENCES IN SMOKING PREVALENCE, Advances in Life Course Research (2014), http://dx.doi.org/10.1016/j.alcr.2014.03.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Manuscript

LIFE COURSE TRANSITIONS AND RACIAL AND ETHNIC DIFFERENCES IN

Department of Sociology and Institute of Behavioral Science, University of Colorado, Boulder

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Elizabeth M. Lawrence*a, Fred C. Pampel a, Stefanie Mollborn a

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SMOKING PREVALENCE

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Running Head: Racial differences in smoking

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*Direct correspondence to Elizabeth Lawrence, 1440 15th Street, Boulder, CO 80302, USA; email: [email protected]; telephone: 303-492-8147

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LIFE COURSE TRANSITIONS AND RACIAL AND ETHNIC DIFFERENCES IN SMOKING PREVALENCE

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Abstract

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This study aims to: (1) describe trajectories in the likelihood of smoking by racial or ethnic group across the transition to adulthood, (2) identify the influence of achieved socioeconomic status (SES) and the nature and timing of adult role transitions, and (3) determine the extent to which achieved SES and adult roles mediate the effects of race and ethnicity on smoking. The analyses use U.S. longitudinal data from the National Longitudinal Study of Adolescent Health (Add Health), which follows a representative national sample over four waves and from ages 11-17 in 1994/95 to 26-34 in 2007/08. Growth curve models compare trajectories of smoking likelihood for white, black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native individuals. While whites have higher rates of smoking than blacks and Hispanics during their teen years and 20s, blacks and Hispanics lose their advantage relative to whites as they approach and enter their 30s. American Indian/Alaska Natives show high rates of smoking at earlier ages and an increasing likelihood to smoke. Although life course transitions are influential for smoking prevalence in the overall U.S. population, SES and the nature and timing of adult role transitions account for little of the gap between whites and black, Hispanic, and American Indian/Alaska Native individuals. Racial and ethnic disparities in adult smoking are independent of SES and life transitions, pointing to explanations such as culturally specific normative environments or experiences of discrimination.

Keywords. Smoking; racial disparities; life course; SES; transition to adulthood; United States

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LIFE COURSE TRANSITIONS AND RACIAL AND ETHNIC DIFFERENCES IN SMOKING PREVALENCE Introduction

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U.S. race and ethnic groups exhibit starkly different, even paradoxical life course trajectories in smoking (Chen & Jacobsen, 2012; Geronimus, Neidert, & Bound, 1993; Griesler & Kandel,

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1998; Griesler, Kandel, & Davies, 2002; Kandel et al., 2004; Pampel, 2008). The trajectories

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contrast most clearly for African Americans and whites, as African Americans smoke considerably less than whites during the teen years, but near parity exists in adulthood (ages >

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18) between whites at 22.1 percent and African Americans at 21.3 percent (Dube et al., 2010; Ellickson et al., 2004). Latino smoking trajectories also differ, with teen rates lower than whites,

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but higher than African Americans, and relatively low adult rates compared to whites and

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African Americans. Asian American smoking patterns are marked by low prevalence both in

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adolescence and in adulthood. Although studies are rare and often limited to samples of specific tribes and locations, the evidence indicates that for Native Americans, high youth smoking initiation (Henderson et al., 2009) and low adult cessation (DHHS, 1998; Fu et al., 2010)

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together define a continuing high prevalence of smoking. These varied patterns of change from adolescence to young adulthood have theoretical importance. For many health phenomena, disparities in young adulthood directly reflect disparities in adolescence. Processes of cumulative advantage occur in which favorable positions produce resources for future gain (DiPrete & Eirich, 2006). Consistent with arguments about the “long arm of childhood” (Hayward & Gorman, 2004; Umberson, Crosnoe, & Reczek, 2010), childhood health influences adult health, and the same processes apply in some ways to smoking (Gilman, Abrams, & Buka, 2003; Lacey et al., 2011; Lynch, Kaplan, & Salonen, 1997).

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And health behaviors in young adulthood are related to many adolescent circumstances, including psychosocial, social support, and family of origin resources (Frech, 2012). Early onset of smoking leads to stronger addiction, inhibits later cessation, and implies the need for policies

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to focus anti-smoking interventions early in life (Chassin et al., 2000; Gilman et al., 2008;

Graham et al., 2006a; Graham, Hawkins, & Law, 2010). Yet, U.S. race and ethnic trajectories in

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smoking involve something different from cumulative advantage: African Americans and, to a

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lesser extent, Latinos lose rather than build on their advantaged levels of low smoking during adolescence. Although Native Americans maintain high levels of smoking and Asian Americans

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maintain low levels of smoking, the patterns of change for other groups emphasize the potential for smoking disparities to change in young adulthood. At least for some race and ethnic groups,

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early adulthood involves more than extending the trajectories begun earlier – smoking disparities

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can take new forms.

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If adolescence sets the stage for but does not irrevocably determine later smoking, then a better understanding of the sources of diverse trajectories from adolescence to adulthood is needed. As youths enter and pass through the transition to adulthood, their newly achieved social

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positions may influence the decision to start or stop smoking. Furthermore, the timing and meaning of life course transitions vary across U.S. racial and ethnic groups, which may contribute to divergent smoking trajectories. For example, African American women, who start childbearing at younger ages on average, tend to delay initiation and take up smoking through their late 20s, after having children. In contrast, white women show both increased childbearing and smoking cessation through the late 20s (Geronimus et al., 1993; Thompson, Moon-Howard, & Messeri, 2011; Weden, Astone, & Bishai, 2006). The purpose of this study is therefore to determine whether underlying life course changes account for the racial or ethnic disparities in

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smoking across the transition to adulthood. This study estimates smoking prevalence across the transition to adulthood among racial and ethnic groups using nationally representative, longitudinal data that has detailed information on adult transitions and achieved status. As no

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prior research has sought to explain U.S. racial differences in smoking over time, this study

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advancing knowledge and spurring future research on this topic.

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identifies and tests theoretically informed hypotheses that may account for divergent trajectories,

Conceptual Framework

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The varied age patterns of smoking across race and ethnic groups in the United States across the transition to adulthood may stem from differences in: 1) achieved socioeconomic

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status (SES), 2) the nature of transitions to adult roles in the domains of school, work, living

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arrangements, union formation, and parenthood, 3) the timing of the transitions to adult roles, or

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4) factors unrelated to SES or life transitions. Pathways from adolescence to young adulthood likely differ by gender, which can shape socioeconomic attainment, the nature and timing of transitions to adult roles, and smoking behaviors. From this perspective, smoking disparities by

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race and ethnicity are more than a fixed outcome – they need to be treated as a process that unfolds over the early life course.

The first life course pathway to shape smoking trajectories during the transition to adulthood relates to race and ethnic differences in achieved SES. Research has established the importance of SES on smoking across cultural contexts, with Western countries demonstrating a strong association between lower SES and smoking behaviors (Power et al. 2005). In the U.S., the socioeconomic disadvantage of most U.S. minority groups relative to whites means they have fewer resources to quit smoking, experience fewer pressures from peers and coworkers to avoid

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smoking, and face more stress, all of which lead to high smoking (DHHS, 1998). Further, disadvantaged minorities are confronted most clearly with the difficulties of economic and family success after high school, when they compete in college and the labor market (Wallace et

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al., 1995; Williams, 2005). Processes of inequality may thus influence smoking disparities most clearly in young adulthood. Failing to advance in schooling, facing unemployment or low wage

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work, or experiencing financial hardship in young adulthood may impede minority groups in

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avoiding or quitting smoking.

The second life course pathway to shape smoking trajectories of race and ethnic groups

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during the transition to adulthood relates to the nature of transitions to adult roles. The human life course can be understood as a series of phases that bring new roles and norms (Elder, 1994).

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The nature of the adult roles tends to differ in ways that reflect social inequality. For example,

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U.S. whites are more likely to marry than blacks, and marriage confers financial and health

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benefits and social esteem (Demo & Cox, 2000; Raley & Sweeney, 2009; Waite, 1995). These adult roles are likely to reduce smoking, as taking on these roles often involve a normative commitment to a lifestyle that is less focused on parties, going out, and same-sex peers and is

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more focused on work, livelihood, spouse, and children (Osgood et al., 1996). Furthermore, the increased monitoring and social control from spouses, co-workers, friends, neighbors, and acquaintance networks that typically come with adult roles encourage conformity to altered norms, discouraging new smoking and encouraging cessation (Christakis & Fowler, 2008). The transition to parenthood may be a particularly important adult role for women, since many avoid smoking during pregnancy for the health of the fetus. Among British women, pregnancy increases the likelihood of quitting smoking across socioeconomic backgrounds (Graham et al., 2010), but those from lower-class backgrounds are still more likely to smoke

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(Spencer 2006). However, some women may temporarily cease smoking during pregnancy and resume post-pregnancy. Strong social norms compel pregnant women not to smoke, and these norms are also enforced by women‟s partners. Once women are no longer pregnant and do not

effect of parenthood may thus differ both by gender and marital status.

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experience these strong norms, avoiding smoking is more difficult (Bottorff et al., 2006). The

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As a third pathway, less advantaged race and ethnic groups tend to have earlier timing of

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transitions to adult roles, such as the transitions to full-time work and parenthood. Although assuming adult roles in work and family should promote non-smoking, the timing and ordering

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of life course transitions can counteract such influences (Neugarten, Moore, & Lowe, 1965; Settersten, 2004), which may put people at risk for smoking. The same type of transition to

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adulthood, such as becoming a parent, may have very different consequences for smoking

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behaviors when done at age 16 versus 26, or when done before marrying versus after. Young

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people who make early transitions are violating age norms about the appropriate timing of the assumption of adult roles (Settersten, 2004). The discrimination and financial hardship experienced by minorities may thus lead to the early assumption of adult roles (Johnson and

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Mollborn, 2009) and therefore to initiation and continuation of smoking. Furthermore, early transitions during adolescence have shown associations with smoking (Wickrama, Wickrama, & Baltimore, 2010).

Lastly, racial and ethnic trajectories may widen independently of SES and life transitions. Racial and ethnic groups may share experiences that both cut across classes and shape their smoking patterns. Age-related smoking norms in particular, may differ across these groups. For example, all members of minority groups facing the stress of discrimination and relative deprivation may use of smoking as a release, self-medication, or form of coping to help regulate

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mood (Marmot, 2004). Indeed, studies have shown that people from various racial/ethnic groups who perceived that they experienced discrimination are much more likely to smoke than others from the same racial/ethnic group who did not perceive discrimination (Borrell et al., 2010; Chae

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et al., 2008). Thus, it may be that the unique experiences of African Americans, even among those with SES that is similar to whites, generate unique patterns of norms such that anti-

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smoking norms strengthen with age among whites but weaken among blacks. In a similar way,

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trajectories for other ethnic groups that show either a rise in smoking during young adulthood

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(AI/AN) or the lack of a substantial decline (Latinos) may relate to smoking norms.

Hypotheses

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To examine the pathways through which race and ethnicity translate diverse patterns of smoking

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behavior over the transition to adulthood, we address three questions:

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1) How do age trajectories of smoking likelihood from adolescence to young adulthood vary across whites, African Americans, Hispanics, Asian/Pacific Islanders, and American Indian/Alaska Natives in the United States?

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We anticipate that age trajectories will differ by race and ethnicity, with the disparities increasing over the life course.

2) How much do SES attainments, the nature of roles taken up in adulthood, and the timing of adult roles affect smoking during young adulthood? We expect that achieved SES, achievement of adult roles, and normative timing of adult roles will reduce likelihood of smoking. 3) How much do attainments, new adult roles, and the timing of new adult roles mediate the influence of race and ethnicity on age trajectories of smoking likelihood?

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We hypothesize that achieved SES, the nature of adult roles, and the timing of adult roles at least partly explain the differences in smoking trajectories across race and ethnic groups.

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Methods Sample

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To answer the questions, we use the National Longitudinal Survey of Adolescent Health (Add

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Health) to examine changes in smoking and varied life positions for white, African American, Hispanic, Asian/Pacific Islanders, and American Indian/Alaska Natives males and females. The

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survey data cover a U.S. nationally representative sample at four time points (1994/1995, 1996, 2001, 2007/2008) and for ages from 11-17 (wave I) to 26-32 (wave IV). With a cluster design

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that samples students within 132 randomly selected middle and high schools and oversampling

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of minority youth, Add Health contains at-home interviews of 20,745 students at wave I and

the respondent surveys.

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15,701 at wave IV (Add Health, 2011). A survey of a parent or guardian in wave I complements

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Measures Outcome

The outcome variable of being a current smoker is defined by self-reports of smoking in the last 30 days. Initial questions ask all respondents whether they have ever tried a cigarette or have ever smoked an entire cigarette. Those answering yes are then asked how many days in the last month they smoked. Combining the two questions leads to the following classification: Current smokers (coded 1) have both tried a cigarette and smoked at least one day in the last month, while current non-smokers have either never tried smoking or not smoked in the last 30 days.

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However, wave III differs from the format for the other waves. Those having tried a cigarette are simply asked if they smoked in the last month and only those answering yes, rather than all those having tried a cigarette, are asked to identify the number of days. It is possible that

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question wording in wave III leads to less effort at recall and thereby underestimates the level of smoking.

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Add Health does not have items to create a detailed history of stopping and starting

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smoking. Despite a few questions on age of starting and, for current non-smokers, on having smoked in the past, Add Health does not measure exact ages of quitting or patterns of tobacco

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use between surveys.1 Further, measures of smoking at the time of survey miss events that occur during the five years between Waves II and III and the six to seven years between waves III and

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IV. Nonetheless, the Add Health data have clear advantages for the study of life course patterns.

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The measurement of smoking at the time of the surveys avoids error-prone recall of behavior that

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occurred several years ago. The measure of whether the subject currently smokes in the first survey at ages 11-17 avoids recall error and does well to represent initiation at young ages, just as the next three surveys do well to represent changes in smoking from the early adolescence to

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young adulthood. With the large Add Health sample and the varying ages of the respondents at each survey year, the four surveys capture smoking at all ages from 11 to 34 and allow for accurate estimation of age-based smoking prevalence without needing a yearly survey for each individual.

Race and Ethnicity Race and ethnicity is represented in the mutually exclusive categories for white, black, Hispanic, Asian/Pacific Islander (A/PI), and American Indian/Alaska Native (AI/AN). Those reporting

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more than one race were assigned to the one category that they reported as best describing their racial background. Because Hispanic ethnicity was asked about separately from race, respondents reporting Hispanic ethnicity were coded as Hispanic and all other categories are

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considered non-Hispanic. Race and ethnicity was taken from wave I, with wave III data

sometimes able to fill in missing observations. Respondents reporting “other race” were omitted

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from analyses due to the small sample and heterogeneity of this group. As Wave III did not

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provide an “other race” category, Wave III data were used for respondents who answered “other

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race” in Wave I but identified as one of the five racial groups in Wave III.

Background Factors

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Analyses also control for immigration status and acculturation, a crucial characteristic for race

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and ethnic comparisons in smoking as foreign-born individuals have lower rates of smoking than

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native-born (Acevedo-Garcia et al., 2005; Baluja, Park, & Myers, 2003). Because we focus on mechanisms that apply to racial and ethnic groups, the findings are not exclusive to either more or less acculturated individuals. Thus, in-depth analysis of the trajectories of native- and foreign-

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born individuals is beyond the scope of this paper. To control for these possible effects, analyses include indicators for location of birth, location of parents‟ birth, and language spoken in the home, a proxy for acculturation. The indicator of household language is taken from the respondent‟s self-report from the first wave, with English coded 1 and all other languages coded 0. Being born in the US is also taken from the respondent‟s self-report in the first wave, with respondents born in the US coded 1 and outside of the US coded 0. To determine whether the responding parent was born in the US, we use the parent‟s report of his or her own nativity status from the parent interview, with the child‟s report about his or her resident or nonresident

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biological mother filling in missing observations. Parents born in the US are coded 1, elsewhere coded 0. Sex, age, parents‟ education level, and parent smoking status are used to control for

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ascribed SES and background factors that may influence smoking. Sex is a dichotomous

variable, with females coded 1, males 0. The age of the individual in years is divided by 10 and

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centered to the sample mean. To allow for a non-linear relationship between age and smoking,

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we also include an age squared term.2 Parents‟ educational level equals the average of the years of education for two parents when available or the reported years of education for one otherwise,

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recoded to the following indicator variables: 1) those who have not completed high school, 2) high school degree, 3) some college, and 4) college degree or higher. The measure comes from

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the parents‟ reports in the wave I parent interview and missing data were filled in from child

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reports in waves I and II. Parent smoking status is a dichotomous variable capturing whether the

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resident mother or the father ever smoked, according to the child‟s responses to the in-home interview in wave I. For 226 individuals, the parent smoking status was missing and was then filled in with the parent response to the question “Do you smoke?” during the parent interview.

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The vast majority of parental respondents were the child‟s mother or other female guardian, as 93% of all responding parents were female. We measure three characteristics at wave I that may spuriously produce relationships between life course changes and smoking. Religiosity, delinquency, and depression all show relationships with achieved SES, adult roles, and smoking (Ellickson, Tucker, & Klein 2001; Gillum 2005; Glassman et al., 1990; Ingersoll-Dayton, Krause, & Morgan 2002; Laub & Sampson, 2003; Lorant et al., 2003). In addition, these variables are endogenous with smoking. Smoking may promote contact with other smokers who tend to have greater participation in

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delinquent activities, limit interactions with religious individuals who are less likely to smoke, and exacerbate issues of depression via stigma and addiction. Using time-invariant controls at the first wave of data minimizes this endogeneity. 3 First, a scale of religiosity comes from two

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variables: how often the respondent attends services and how important religion or religious faith is. Combined into a standardized scale, the two items have an alpha scale reliability of .78.

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Second, respondents were asked 8-15 questions about how often they engaged in delinquent acts.

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These items were combined to create a standardized scale of delinquency at wave I with an alpha scale reliability coefficient of .85. Third, a scale of depression comes from a series of statements

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asking how often the respondent had particular feelings in the last week. The alpha scale reliability coefficient is .87. Both the delinquency and depression scales are highly skewed and

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Achieved SES

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logged before using in the models.

We measure achieved SES in the transition to adulthood through time-varying measures of educational attainment, income, and wealth. The respondent‟s educational attainment in years

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was collected at each of the four waves. A categorical variable was used to represent personal earnings, with all waves converted to 2010 dollars using the ratios provided by the Consumer Price Index for all Urban Consumers. Earnings were divided into five categories, with one category set to zero earnings, one category for missing earnings information, and the other respondents divided into tertiles of $1-$5,000, $5,001-$18,223, and $18,224 and above. As many individuals are missing personal earnings, we categorize the variable to provide a missing category. For wealth, a dichotomous variable to show whether the respondent reported owning a home, condominium, or other residence was created for each wave, with waves I and II set to 0.

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Adult Role Transitions The gaps between the last three waves of the Add Health make it hard to measure the precise

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timing of life transitions. However, the adult roles stemming from these transitions can be

measured directly. For example, continuation into advanced school and exit of schooling into the

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labor force affect current school attendance and labor force status. Similarly, marriage, birth of a

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child, and moving to a new residence affect current marital status, number of children, and living arrangements. Whether or not the respondent was in school was collected at each wave (in

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school equal to 1, not in school equal to 0). Following Staff et al. (2010), a categorical variable was used to capture work status, indicating whether someone is not working, is working in a

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nonprofessional job, or is working in a professional job. For waves III and IV, the Standard

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Occupational Classification System was used to categorize occupational status. Since individuals

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in waves I and II were in high school, jobs during these waves were defined as nonprofessional. A binary variable captures whether or not the respondent lives with his or her parents at each wave. A categorical variable represents whether or not the respondent is married and living with

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a son or daughter during the time of the interview at all waves. The reference group comprises those who are not married and without children, and the three other groups include those that are married with children, not married with children, and married without children. Preliminary models indicated that cohabiting respondents showed similar effects to those unmarried and not cohabiting and were therefore not treated separately.

Statistical Analysis For both females and males, the analysis will examine the influence on smoking of: 1) time-

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invariant background factors such as parental education, 2) time-varying variables relating to achieved SES and adult roles, and 3) the timing-based interactions of age and adult roles4. The models are separated by sex, as patterns in smoking have been shown to differ for males and

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females, and tests show substantial improvement in the BIC statistics from estimates of pooled models (Escobedo & Peddicord, 1996). The strategy is to first compare trajectories across racial

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and ethnic groups and then to examine the influence on smoking of mediating variables related

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to the nature and timing of new roles and achieved SES in young adulthood. The contribution of achieved SES and adult roles to reduced racial/ethnic differences in trajectories of smoking

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likelihood will show in their ability to explain the differences in age trajectories by race. A multilevel, growth-curve framework is well suited to examine variation in smoking

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likelihood trajectories, as this approach allows us to look at inter-individual differences in intra-

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individual change. The multilevel model is necessary since the multiple time points of

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information for each individual would violate the assumption of independence for ordinary least squares regression and understate the standard errors. Age serves as the level-1 unit and persons as the level-2 unit. The time-varying variables

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are included in the level-1 models, and the time-invariant variables are included in the level-2 models. The models also allow for use of subjects with incomplete data, a key need given the loss of cases through attrition. In particular, wave II excludes wave I high school seniors, but the models retain cases with incomplete data. As the outcome of smoking is dichotomous, we use a logistic approach for multilevel models. The basic multilevel model takes the following form for person i at time t: log[Pr(Sti)/(1-Pr(Sti))] = β0i + β1i (Ati – L) + β2i (Ati – L)2 + Σ βkiXkti

(1)

β0i = γ00 + Σ γ0jWji + u0i ,

(2a)

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β1i = γ10 + Σ γ1jWji + u1i ,

(2b)

β2i = γ20 + Σ γ2jWji + u2i ,

(2c)

βki = γk0 .

(2d)

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The coefficients β1i and β2i, both treated as random effects, describe, respectively, the linear increase (at the centered value L of age 20) and rate of acceleration (or deceleration) of the

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smoking trajectory for each individual. The βki coefficients for k level-1 time-varying variables

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are treated as fixed (i.e., βki = γk0). The γ coefficients for j time-invariant W variables show how stable background characteristics such as race or ethnicity modify the level of smoking in 2a, the

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age trajectories in smoking in 2b-2c. The random residuals (u0i, u1i, u2i) are assumed to be independent and normally distributed, with mean 0 and constant variance.

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The model estimates a distribution of trajectories for all sample individuals. The focus

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on within-individual change thus controls for unmeasured stable differences across individuals.

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Alternatively, latent growth models that classify trajectories into a small set of discrete groups could be used (Chassin et al., 2000; Costello et al., 2008). However, the discrete-group modeling approach deals less well with time-varying, life course variables.

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For all models, odds ratios of smoking with 95% confidence intervals are reported. Models were estimated using the “xtmelogit” command in Stata 12 (StataCorp, 2011). With random effects for the intercept, slope of age, and slope of age squared, some simplifying assumptions about the variance components were needed to obtain reliable estimates. The models assign one unique variance parameter per random effect but assume covariance parameters are zero. In addition, software limits prevent controlling for complex sampling design.

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Results Descriptive Statistics Table 1 lists descriptive statistics for the model variables, both pooled across waves and

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separately by wave. On average, about 34 percent of the sample smoked in the last month, and the average rises over time across all waves, except in wave III (which may be underestimated

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slightly because of a different form of the smoking question). However, to describe the

Table 1 About Here

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trajectories correctly, smoking should be matched to age rather than to wave.

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As shown by statistics for the background variables, the sample is largely white and ranges from an average age of 15.4 in wave I to 28.3 in wave IV. The parents of respondents

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most commonly have some college education and have ever smoked or currently smoke. The

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time-varying variables show increases across the waves in years of school and work in

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professional occupations, and decreases in school attendance and having no occupation. In the first wave, most respondents live with their parents, are single, and have no children, but by the last wave, most are married, have kids, or both. In terms of finances, substantial proportions

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have significant earnings or own a residence by wave IV. Wave I controls of religiosity, delinquency, and psychological depression may tap underlying traits associated with life positions and smoking.

Age Trajectories of Smoking Likelihood Table 2 displays the results from multilevel models estimating the relationship between race and smoking over time, controlling for other variables. The initial model indicates the influence of race on smoking, while the racial trajectory models show how smoking likelihood trajectories

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diverge for individuals of different racial or ethnic backgrounds. For both males and females, the initial models demonstrate that black [female OR=.09; male OR=.27], Hispanic [female OR=.30; male OR=.49], and A/PI [female OR=.46; male OR=.63] individuals are significantly less likely

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to smoke compared to whites. For both males and females, the age term is above one [female OR=1.61; male OR=3.44] and the age squared term is below one [female OR=.30; male

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OR=.26], resulting in an inverse U-shape for smoking: smoking becomes more likely as one ages

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in the teens and early 20s, and then begins to fall in the mid-20s. Other covariates indicate that individuals whose parents are native born, have ever smoked, and less highly educated show

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increased risk of smoking. In addition, individuals who are less religious, more delinquent, and more depressed at wave I are more likely to smoke.

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Table 2 About Here

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The racial trajectory models then allow the likelihoods to vary by racial background. The

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mostly significant interaction terms indicate that allowing age and race to interact provides more accurate smoking trajectories. As demonstrated in Panels A in Figures 1 and 2, for both males and females, the age interaction ratios below one and age squared interaction ratios above one

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show that the inverse U-shape is flatter for Hispanics than whites. Black males and females have an increasing likelihood of smoking over time, as both the age and age squared interactions are above one. The female racial trajectory model also illustrates that smoking among AI/AN women is increasing strongly at age 34, with an odds ratio for the age interaction of 1.87 and for the age squared interaction of 10.17, resulting in much higher rates at these later ages. The increase in Panel A of Figure 1 should be interpreted with consideration of the nonsignificance of the age interaction and the wide confidence intervals for these ratios. AI/AN men do not display statistically significant age interaction terms, but as this population has different smoking

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patterns from whites (DHHS, 1998; Fu et al., 2010), the trajectory is included in the figure. Though A/PI were less likely to smoke in the initial model, the trajectories of A/PI men and women do not differ significantly from those of whites. Thus, the figures represent predicted

Figures 1 and 2 About Here

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population composition of the sample.

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probabilities that proportionally combine the odds ratios of whites and A/PI based on the

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Table 3 shows the results when transitions and achieved SES are also considered. The first model shows expected effects of transitions and achieved SES for women, with reduced

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likelihood of smoking for those in non-professional jobs, more highly educated, and who are homeowners. Similarly, for males, effects are consistent with expectations, with those in school,

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in professional occupations, more highly educated, and who are homeowners smoking less.

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Interestingly, having children is only influential for women, not men, which may be the result of

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women quitting during pregnancy. Married men and women exhibit lower likelihoods, although the protective effect is stronger for women.

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Table 3 About Here

Comparing the terms that multiply each racial group by age and age squared in Tables 2 and 3, the interaction coefficients for Hispanic men and women and black men mostly diverge further from one when role measures and achieved SES are added in, indicating that the gaps are not reduced. For example, adding in the new covariates changes the odds ratios of Hispanic women from .65 and 2.69 for the age and age squared interactions to .49 and 2.76. For black women, the odds ratios have converged to one slightly, going from 1.70 and 3.41 to 1.13 and 3.15, but the overall trend for this group has not changed much. Panels B in Figures 1 and 2 display the trajectories for each racial group from the Table 3 models. Although the shapes of these figures

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change slightly with the addition of these covariates, the racial differences appear quite similar to the earlier figures, demonstrating that the transition to adulthood and achieved SES do not explain well the differences among the different racial groups. However, role measures and

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achieved SES do appear to be important predictors of smoking across races, as indicated by the significant terms and improved model fit from the racial trajectory models in Table 2 to the first

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models in Table 3. The Bayesian Information Criterion (BIC) decreased from 27414 to 26915 for

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females and from 27952 to 27486 for males.

Table 4 considers how the timing of transitions may influence smoking patterns by

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adding in interactions between age and each transition. It does not appear that the influence of the transitions is much affected by age, as most of the interaction terms are nonsignificant. The

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exceptions are that for men and women, being unmarried with children (compared to unmarried

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without children) at older ages does less to reduce smoking than at younger ages [female main

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effect OR=.61; female interaction effect OR=1.77; male main effect OR=.81; male interaction effect OR=2.07]. In addition, significant interaction terms between age and nonprofessional occupations for both men and women indicate that the increased likelihood of smoking for those

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in nonprofessional occupations is reduced at older ages. However, most of the interaction terms are nonsignificant and comparisons of the BIC statistics for these models to the Table 3 models do not show improved model fit. Thus, the effects of these mediators do not vary much across age, and adding age and age squared interactions for all the mediators would unduly complicate the models while doing little to improve them. Table 4 About Here These analyses have indicated that race and ethnicity, role transitions, and achieved SES are influential on smoking, but that the effect of race is not reduced much by the addition of role

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transitions and achieved SES. Additional analyses also tested for the influence of race on each of the possible mediating variables through growth curve models. These models predicted each of the role transitions and achieved SES variables, stratified by sex and controlling for age, native

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English, nativity status, and parent education. We found significant, consistent, and strong race differences for occupational status, living with parents, family structure, years of education, and

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home ownership. Males showed decreased levels of personal earnings for nonwhites, but females

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did not show significant differences. Consistent patterns across race did not emerge for being enrolled in school. These findings suggest that, while most of the mediators differ across race

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and ethnicity categories, they do not account for divergent race trajectories in likelihood of

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smoking.

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Discussion

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While studies have described the divergent patterns of smoking prevalence across racial and ethnic groups in the United States, they have done little to test possible explanations of the patterns. Our study thus takes a first step in putting forth and testing hypotheses for divergent

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trajectories of smoking likelihood among racial and ethnic groups. As achieved SES and the nature and timing of adult roles are two major differences in the experiences of adolescents and young adults from different racial/ethnic groups, our study aimed to identify the extent to which achieved SES and adult roles mediate the influence of race and ethnicity on age trajectories of smoking likelihood. The Add Health data, which include high-quality longitudinal measures from ages 11 to 34, are well suited for these goals. Besides describing life course trajectories in smoking, the data allow us to examine the influence of numerous life course variables on the varied trajectories across racial and ethnic groups. The data and approach improve substantially

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on existing studies of smoking. The results reveal that race and ethnic groups differ markedly in their patterns of smoking prevalence. While whites have higher rates of smoking than blacks and Hispanics during the teen

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years and 20s, blacks and Hispanics converge with whites as they progress into adulthood. Black men and women continue to increase their likelihood of smoking throughout the study period.

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Blacks and Hispanics thus lose their advantage relative to whites, but not until adulthood. AI/AN

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smoking patterns across the transition to adulthood, which had rarely been documented, differ greatly from those of the other groups, showing both high rates at earlier ages and much higher

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likelihood of smoking at the older ages. However, the patterns for AI/AN males were not statistically significant and the patterns for females show a large confidence interval. Data with a

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larger sample may be able to describe smoking for this racial group more accurately.

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Perhaps surprisingly given existing racial and ethnic disparities in socioeconomic status

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and differences in the timing and nature of transitions to adulthood, life transitions, and achieved SES explained little to none of the differences among racial groups. Interestingly, these findings contrast with additional analyses we have completed (Pampel, Mollborn, & Lawrence, 2014).

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The additional analyses indicate that SES attainment and new roles in early adulthood explain a substantial portion of differences in smoking trajectories by parental education. Life transitions in early adulthood thus explain differences in smoking by family SES but not by race and ethnicity. Further, background SES was not influential for the racial and ethnic disparities, either. Supplemental analyses (not shown) comparing trajectories with and without controls for parent education did not differ, revealing the independence of both ascribed and achieved SES on racial and ethnic differences in smoking. Our findings thus have important theoretical and policy implications. Theoretically, our

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results confirm that the transition to adulthood may differ across racial and ethnic groups beyond adult role transitions and achieved SES. We suggest two related mechanisms for racial effects on smoking, neither of which can be tested with our data. First, cultural norms about smoking may

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not only change from adolescence to adulthood but do so differently for race and ethnic groups. Second, racialized experiences of discrimination may lead to the use of smoking as a way of

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coping with stress. Future research that better describes how normative environments differ

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across racial and ethnic groups and how these environments affect smoking could help identify why smoking trajectories differ among these groups. Our results also suggest that policies

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designed to reduce smoking across race and ethnicity should direct efforts toward motivations for smoking, rather than focus on programs that are specific to age, adult role, or SES.

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The standing of cigarette use as the largest source of premature mortality in high-income

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nations (CDC, 2008; DHHS, 2004) justifies efforts to reduce smoking among disadvantaged

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minorities as critical to improving health and longevity (Fagan et al., 2004; Fiori et al., 2004; NIH, 2000). Public health interventions designed to prevent smoking initiation should therefore consider targeting a broader range of ages, as focusing efforts on adolescents and the youngest

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adults ignores more problematic older ages for blacks, Hispanics, and AI/AN. Policy makers should address motivations to smoke, which likely vary among the normative environments of different racial and ethnic groups, and provide interventions that consider a variety of reasons and structural conditions for smoking patterns, including racial discrimination. Evaluations of policies and programs should also consider that some interventions may have differing levels of effectiveness for individuals of different racial or ethnic background (Cowell et al., 2009). Further, health interventions would benefit from future research that determines how and why cultural norms may differ and the mechanisms with which cultural norms or racial discrimination

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may influence starting, continuing, or quitting smoking. The age range of subjects available from the four longitudinal waves of the Add Health data – 11 to 34 years old – provides a span sufficient to capture changing trajectories of smoking

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likelihood across the transition to adulthood. By the mid-20s, the smoking of persons from more advantaged family backgrounds, with higher socioeconomic attainments, and in successful adult

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roles declines in ways that creates new forms of differentiation. However, to obtain a more

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complete picture of life course patterns of smoking, subjects need to be followed to older ages, which will be possible with the release in coming years of Wave V of Add Health. In addition,

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though the multilevel approach allows us to include all respondents with at least two waves of data and mitigate the effects of attrition, missing data limit the sample and thus, the strength of

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our conclusions.

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Otherwise, the results are limited by the lack of a continuous history of smoking over the

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full range of years covered by the Add Health surveys. Because of the gaps in time between waves, we cannot directly match the timing of life course transitions with changes in smoking, so instead we take life course characteristics at each time point as determinants of smoking at the

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same time point. That the smoking trajectories match what has been previously documented for patterns among white, African American, Hispanic, A/PI, and AI/AN groups supports the approach. Thus, using Add Health improves considerably on cross-sectional approaches to smoking disparities and allows us to consider life course changes. The results support the conclusion that racial and ethnic patterns of smoking are independent of achieved SES and life transitions, and supplemental models revealed that these patterns are also separate from ascribed SES. These findings are specific to the United States, but the implications may be applicable more broadly. The motivational mechanisms that are

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important for U.S. racial and ethnic disparities in smoking may operate similarly for marginalized groups in other contexts. This study builds on a body of research asserting the general importance of long-term disadvantage and distal determinants for smoking patterns

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(Graham et al., 2006b). In addition, this study challenges a narrow focus on social class in

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smoking research, as there may be influential contexts not only beyond, but unrelated to SES.

1

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Endnotes

As 70% of the sample does not smoke at all, any smoking count variable would not be

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normally distributed and we cannot estimate a linear model. In addition to its statistical advantages, logistic regression makes theoretical sense. As any amount of smoking reflects a

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health risk, it is reasonable to separate smokers from non-smokers. As MacCallum et al.

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(2002:38) note, dichotimization is justified in the case of smoking, as there is a skewed

2

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distribution and a presence of two behaviorally meaningful groups, smokers and non-smokers. When the age squared terms are created, the values can be quite large. Since we are using

multilevel models that allow the slopes of age and age squared to vary randomly, large age

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squared values make the variance around the slope of age squared numerically small. Thus, we divide age by 10 in order to facilitate model convergence. 3

Although these measures are available at all waves, the measures change in ways that limit their

comparability. Further, supplemental models using time-varying measures for religiosity, delinquency, and depression displayed nearly identical results to those presented here. We therefore control for the Wave I measures to capture the influence of these indicators on smoking, but do not examine them as mediators. 4T

here are gaps between waves of data that obscure the precise timing of events, but the age and

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role at each wave captures whether someone takes on these roles earlier or later in the life course and most importantly, differentiates between normative (e.g., marriage or childbearing during the

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mid-to-late 20s) and non-normative timing (e.g., marriage or childbearing during the teen years).

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ACKNOWLEDGEMENTS

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This project received funding and administrative support from the University of Colorado Population Center, which is funded by the Eunice Shriver National Institute of Child Health and Human Development (grant NICHD R21 HD051146). This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations.

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socialization. American Journal of Sociology, 70(6),710-717. Osgood, D.W., Wilson, J.K., O‟Malley, P.M., Bachman, J.G., & Johnston L.D. (1996). Routine activities and individual deviant behavior. American Sociological Review, 61, 635-655. Pampel, F.C. (2008). Racial convergence in cigarette use from adolescence to the mid-thirties. Journal of Health and Social Behavior, 49, 484-498. Pampel, F., Mollborn, S., & Lawrence, E. (2014). Life course transitions in early adulthood and SES disparities in tobacco use. Social Science Research, 43, 45-59. Power, C., Graham, H., Due, P., Hallqvist, J., Joung, I., Kuh, D., & Lynch, J. (2005). The

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contribution of childhood and adult socioeconomic position to adult obesity and smoking behavior: an international comparison. International Journal of Epidemiology, 34, 335-344 Raley, R. K. & Sweeney, M.M. (2009). Explaining race and ethnic variation in marriage:

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directions for future research. Race and Social Problems, 1,132-142.

Settersten, R.A., Jr. (2004). Age structuring and the rhythm of the life course. In J. T. Mortimer,

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& M. J. Shanahan (Eds.), in Handbook of the life course (pp. 81-98). New York: Kluwer

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Academic/Plenum Publishers.

Spencer, N. (2006). Explaining the social gradient in smoking in pregnancy: Early life course

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accumulation and cross-sectional clustering of social risk exposures in the 1958 British national cohort. Social Science & Medicine, 62(5), 1250-1259.

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Staff, J., Schulenberg, J.E., Maslowsky, J., Bachman, J.G., O'Malley, P.M., Maggs, J.L., &

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Johnston, L.D. (2010). Substance use changes and social role transitions: Proximal

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developmental effects on ongoing trajectories from late adolescence through early adulthood. Development and Psychopathology, 22, 917-932. StataCorp. (2011). Stata statistical software: Release 12. College Station TX: StataCorp LP.

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Thompson, A.B., Moon-Howard, J., & Messeri, P.A. (2011). Smoking cessation advantage among adult initiators: Does it apply to black women? Nicotine & Tobacco Research, 13, 15–21.

Umberson, D., Crosnoe, R., & Reczek, C. (2010). Social relationships and health behavior across the life course. Annual Review of Sociology, 36, 139-157. Waite, L.J. (1995). Does marriage matter? Demography, 32, 483-507. Wallace, J., Bachman, J. G., O'Malley, P. M. & Johnston, L. D. (1995). Racial/ethnic differences in adolescent drug use. In G. Botvin, S. Schinke, & M. Orlandi (Eds.), Drug abuse

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prevention with multiethnic youth (pp. )Thousand Oaks, CA: Sage. Weden, M.M., Astone, N.M., & Bishai, D. (2006). Racial, ethnic, and gender differences in

in the US. Social Science and Medicine, 62, 303-316.

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smoking cessation associated with employment and joblessness through young adulthood

Wickrama, T., Wickrama, K.A.S., & Baltimore, D. L. (2010). Adolescent precocious

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development and young adult health outcomes. Advances in Life Course Research, 15,

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121-131.

Williams, D.R. (2005). The health of U.S. racial and ethnic populations. Journals of Gerontology

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d

M

an

Series B Psychological Sciences and Social Sciences, 60 (Spec No 2), S53–S62.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

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Adult Roles In school Professional occupation Not professional occupation No occupation Living with parents Married with kids Married without kids Not married with kids Not married without kids

d

-0.01 (1.00) 0.60 (0.36) 0.54 (0.49)

te

Controls Religiosity scale (-1.8 – 1.3) Logged delinquency scale (0.20 – 2.40) Logged depression scale (-.54 – 2.10)

0.63 0.13 0.43 0.45 0.63 0.10 0.05 0.07 0.78

0.38

21.76 0.69 0.15 0.11 0.04 0.01 0.93 0.94 0.88 0.52 0.20 0.21 0.40 0.19 0.67

28.30 0.70 0.15 0.11 0.03 0.01 0.94 0.95 0.89 0.52 0.20 0.22 0.40 0.18 0.67

-0.02 0.60 0.54

-0.01 0.61 0.54

0.00 0.60 0.54

-0.02 0.60 0.54

100.00 0.00 0.33 0.67 0.96 0.00 0.00 0.01 0.99

0.92 0.00 0.40 0.60 0.94 0.00 0.00 0.02 0.98

0.38 0.18 0.52 0.30 0.43 0.10 0.08 0.11 0.72

0.16 0.34 0.48 0.18 0.16 0.30 0.14 0.16 0.41

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15.96 0.68 0.15 0.12 0.04 0.01 0.93 0.94 0.87 0.50 0.20 0.22 0.40 0.19 0.67

us

15.38 0.68 0.15 0.12 0.04 0.01 0.93 0.94 0.87 0.50 0.20 0.22 0.40 0.18 0.67

M

20.18 (5.55) 0.69 0.15 0.12 0.04 0.01 0.93 0.94 0.88 0.51 0.20 0.22 0.40 0.18 0.67

an

Ascriptive variables Age (11–34) White Black Hispanic Asian/Pacific Islander American Indian/Alaska Native Native English Speaker Born in US Parent born in US Female Parent education
Wave IV

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Table 1. Weighted descriptive statistics of Add Health sample, Waves I-IV. Mean by Wave Mean (SD) Wave I Wave II Wave III Dependent Variable Current smoker 0.34 0.28 0.35 0.35

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Achieved SES Years of education (6–22) 11.50 (2.76) 9.38 9.95 13.13 14.09 Personal earnings $0 0.19 0.30 0.27 0.11 0.07 Personal earnings $1-$5000 0.28 0.43 0.40 0.22 0.05 Personal earnings $5001-$18223 0.26 0.24 0.29 0.33 0.18 Personal earnings $18224+ 0.25 0.02 0.03 0.30 0.68 Personal earnings missing 0.02 0.01 0.01 0.04 0.02 Residence owner 0.13 0.00 0.00 0.13 0.43 Source: National Longitudinal Study of Adolescent Health. Notes: N=56,041 person-waves (16,939 individuals). Statistics adjust for weighting. Ranges for continuous variables are given in parentheses. Standard deviations (SD) are given for continuous variables.

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Table 2. Odds Ratios and Confidence Intervals for Multilevel Logistic Regression Models of Current Smoking: Ascriptive Variables Initial Model

0.31 *** 0.28,0.35

0.30 *** 0.27,0.33

1.61 *** 1.45,1.78 0.30 *** 0.25,0.36

3.44 *** 3.08,3.85 0.26 *** 0.22,0.31

1.52 *** 1.34,1.72 0.21 *** 0.17,0.26

3.15 *** 2.74,3.62 0.19 *** 0.15,0.24

0.09 0.30 0.46 0.49 2.54

0.27 0.49 0.63 1.15 1.59

*** 0.07,0.11 *** 0.23,0.39 *** 0.31,0.67 0.22,1.12 *** 1.76,3.69

*** 0.22,0.33 *** 0.39,0.63 ** 0.45,0.88 0.56,2.37 ** 1.14,2.22

1.71 ** 1.19,2.45 1.78 *** 1.32,2.41 2.18 *** 1.85,2.56

1.71 ** 1.24,2.36 1.44 ** 1.09,1.89 1.73 *** 1.49,2.01

1.18 0.94,1.49 0.75 ** 0.61,0.93 0.60 *** 0.47,0.77 0.69 *** 0.64,0.75 20.60 *** 16.02,26.50 1.85 *** 1.58,2.16

M an

0.30 *** 0.27,0.33

0.09 0.31 0.46 0.51 2.53

*** 0.07,0.11 *** 0.24,0.41 *** 0.31,0.67 0.23,1.16 *** 1.75,3.66

0.27 0.51 0.61 1.21 1.57

*** 0.22,0.33 *** 0.40,0.65 ** 0.44,0.86 0.59,2.51 ** 1.13,2.18

1.70 ** 1.18,2.43 1.76 *** 1.30,2.38 2.17 *** 1.85,2.55

1.70 ** 1.23,2.35 1.43 * 1.09,1.89 1.73 *** 1.49,2.01

1.05 0.84,1.32 0.86 0.71,1.06 0.55 *** 0.43,0.70

1.18 0.94,1.48 0.75 ** 0.61,0.93 0.60 *** 0.47,0.77

1.04 0.83,1.31 0.86 0.70,1.05 0.54 *** 0.43,0.69

0.85 *** 0.79,0.91 9.25 *** 7.64,11.19 1.60 *** 1.37,1.88

0.69 *** 0.64,0.75 20.43 *** 15.89,26.26 1.85 *** 1.58,2.16

0.85 *** 0.79,0.91 9.24 *** 7.64,11.18 1.61 *** 1.37,1.88

1.70 3.41 0.65 2.69 1.23 1.14 1.87 10.17

1.98 2.97 0.62 2.36 1.58 1.06 1.33 3.13

ed

Age/10 Age/10 Squared Race (White) Black Hispanic A/PI AI/AN Native English Nativity Born in US Parent born in US Parent ever smoked Parent Education (< HS) High school degree Some college College degree or higher Controls Religiosity Delinquency Depression Interactions Black * age Black * age2 Hispanic*age Hispanic* age2 A/PI*age A/PI* age2 AI/AN*age AI/AN* age2

0.31 *** 0.28,0.35

ce pt

Fixed Effects Constant

Racial Trajectory Model Females Males OR (95% CI) OR (95% CI)

Males OR (95% CI)

us

Females OR (95% CI)

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

*** *** ** ***

*

1.28,2.24 2.11,5.52 0.48,0.88 1.61,4.51 0.75,2.01 0.51,2.56 0.61,5.78 1.32,78.48

*** *** ** ***

1.49,2.63 1.85,4.78 0.46,0.84 1.44,3.87 1.00,2.49 0.52,2.15 0.48,3.73 0.56,17.54

Estimate Estimate Estimate Estimate Random Effects SE SE SE SE 3.52 3.85 3.14 3.51 Variance of age 0.35 0.37 0.33 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Variance of age2 Variance of constant 6.57 0.31 5.75 0.28 6.55 0.31 5.75 0.28 *** p < .001; ** p < .01; * p <.05 Source: National Longitudinal Study of Adolescent Health (Female N person = 8,760; Female N person-time = 29,664; Male N person = 8,231; Male N person-time = 26,885) Notes: Age, age squared, English speaker status, nativity, parent smoking status and parent education variables are centered to their sample means.

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0.08 0.30 0.43 0.44 2.40

0.26 0.51 0.68 1.16 1.55

*** *** *** * ***

0.06,0.10 0.23,0.39 0.30,0.63 0.20,0.99 1.66,3.46

Achieved SES Years of education Personal earnings (No earnings) Personal earnings $1-$5000 Personal earnings $5,001-$18,223 Personal earnings $18,224+ Personal earnings missing Own residence

*** 0.22,0.32 *** 0.40,0.64 * 0.49,0.94 0.57,2.34 ** 1.12,2.13

1.70 *** 1.24,2.33 1.35 * 1.03,1.76 1.66 *** 1.44,1.92

1.29 * 0.88 0.79

1.08 1.00 0.74 *

0.86,1.34 0.82,1.22 0.58,0.94

an

0.85,1.51 *** 1.93,5.14 *** 0.36,0.66 *** 1.63,4.67 0.58,1.56 0.55,2.84 0.37,3.48 * 1.45,85.63

1.48 2.81 0.52 2.24 1.45 1.13 0.93 3.31

** *** *** **

M

1.13 3.15 0.49 2.76 0.95 1.24 1.13 11.13

1.02,1.62 0.71,1.09 0.61,1.01

us

1.78 ** 1.24,2.54 1.68 *** 1.24,2.27 2.09 *** 1.78,2.45

0.72 *** 0.67,0.78 18.67 *** 14.54,23.97 1.69 *** 1.44,1.97

1.11,1.97 1.74,4.52 0.38,0.70 1.37,3.67 0.92,2.28 0.55,2.29 0.34,2.57 0.60,18.33

0.88 *** 0.82,0.94 8.18 *** 6.79,9.84 1.46 *** 1.25,1.70

0.85,1.13

0.82 ** 0.71,0.95

0.88 0.73,1.06 1.25 *** 1.10,1.42 1.10 0.95,1.26

0.59 *** 0.48,0.72 1.17 * 1.03,1.33 1.13 0.99,1.30

0.34 *** 0.27,0.42 0.39 *** 0.30,0.50 0.73 ** 0.61,0.88

0.70 ** 0.56,0.87 0.62 *** 0.49,0.79 1.19 0.92,1.54

0.78 *** 0.75,0.81

0.84 *** 0.81,0.87

1.01 0.87,1.16 1.25 ** 1.06,1.48 0.97 0.80,1.19 0.77 0.54,1.11 0.67 *** 0.56,0.80

1.13 0.97,1.31 1.60 *** 1.34,1.90 1.08 0.89,1.32 0.79 0.56,1.12 0.72 *** 0.60,0.86

0.98

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8.34 *** 6.25,11.14 0.15 *** 0.12,0.21

d

Role Measures In school Occupational Status (No occupation) Professional occupation Not professional occupation Living with parents Family (not married without kids) Married with kids Married without kids Not married with kids

9.02 *** 6.68,12.20 0.12 *** 0.09,0.17

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Age/10 Age/10 Squared Race (White) Black Hispanic A/PI AI/AN Native English Nativity Born in US Parent born in US Parent ever smoked Parent Education (less than high school) High school degree Some college College degree or higher Interactions Black * age Black * age2 Hispanic*age Hispanic* age2 A/PI*age A/PI* age2 AI/AN*age AI/AN* age2 Controls Religiosity Delinquency Depression

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Table 3. Odds Ratios and Confidence Intervals for Multilevel Logistic Regression Model of Current Smoking: Time-Varying Variables Females Males OR (95% CI) OR (95% CI) Fixed Effects Constant 0.34 *** 0.31,0.38 0.29 *** 0.26,0.32

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Estimate Estimate Random Effects SE SE 3.11 3.32 Variance of age 0.33 0.35 2 0.00 0.00 0.00 0.00 Variance of age Variance of constant 6.35 0.31 5.27 0.26 *** p < .001; ** p < .01; * p <.05 Source: National Longitudinal Study of Adolescent Health (Female N person =8,740; Female N person-time = 29,437; Male N person=8,199; Male N person-time = 26,661) Notes: Age, age squared, English speaker status, nativity, parent smoking status, parent education, role measure, achieved SES, and control variables are centered to their sample means.

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1.13 3.13 0.49 2.75 0.94 1.25 1.12 11.28

Role Transition Interactions In school*age Occupational Status (No occupation) Professional occ*age Not professional occ*age Living with parents *age Family (not married without kids) Married with kids *age Married without kids*age Not married with kids*age

0.94

0.80,1.10

0.88 0.73,1.06 1.25 *** 1.10,1.42 1.10 0.96,1.27

1.11 3.32 0.49 2.86 0.94 1.28 1.10 10.51

0.84,1.48 *** 2.04,5.43 *** 0.36,0.66 *** 1.69,4.84 0.57,1.56 0.56,2.91 0.36,3.38 * 1.36,80.98

0.96

0.34 *** 0.27,0.42 0.39 *** 0.30,0.50 0.73 *** 0.60,0.88 1.19

0.83,1.11

0.75 * 0.57,0.99 1.22 ** 1.07,1.39 1.07 0.93,1.24

ce pt

Role Transitions and SES In school Occupational Status (No occupation) Professional occupation Not professional occupation Living with parents Family (not married without kids) Married with kids Married without kids Not married with kids

0.85,1.50 *** 1.92,5.10 *** 0.36,0.66 *** 1.63,4.66 0.57,1.55 0.55,2.84 0.36,3.45 * 1.47,86.77

8.99 *** 6.60,12.23 0.13 *** 0.09,0.18

0.33 *** 0.26,0.40 0.38 *** 0.30,0.50 0.73 ** 0.60,0.88

Males Model 2 OR (95% CI)

Model 3 OR (95% CI)

0.29 *** 0.26,0.32

0.28 *** 0.25,0.32

8.53 *** 6.38,11.41 0.14 *** 0.10,0.19

8.45 *** 6.27,11.38 0.18 *** 0.13,0.24

8.55 *** 6.32,11.57 0.15 *** 0.11,0.21

1.48 2.81 0.52 2.24 1.46 1.13 0.93 3.30

1.11,1.97 1.74,4.52 0.38,0.70 1.37,3.67 0.93,2.29 0.56,2.31 0.34,2.57 0.60,18.33

1.41 2.86 0.51 2.29 1.41 1.12 0.90 3.38

1.48 2.71 0.52 2.24 1.45 1.14 0.94 3.27

0.73,0.99

0.80 ** 0.69,0.92

0.81 ** 0.71,0.94

0.88 0.73,1.07 1.25 *** 1.10,1.42 1.08 0.93,1.25

0.59 *** 0.48,0.72 1.16 * 1.02,1.32 1.13 0.98,1.30

0.53 *** 0.39,0.72 1.13 0.99,1.29 1.11 0.97,1.27

0.59 *** 0.48,0.72 1.16 * 1.02,1.32 1.13 0.97,1.32

0.35 *** 0.25,0.49 0.33 *** 0.23,0.49 0.61 *** 0.48,0.76

0.69 ** 0.56,0.86 0.62 *** 0.49,0.79 1.18 0.91,1.53

0.72 ** 0.58,0.89 0.62 *** 0.49,0.79 1.20 0.93,1.55

0.92 0.60,1.41 0.50 ** 0.31,0.79 0.81 0.52,1.25

8.77 *** 6.47,11.88 0.11 *** 0.08,0.16

M an

8.86 *** 6.54,11.99 0.14 *** 0.10,0.19

ed

Age/10 Age/10 Squared Interactions Black * age Black * age2 Hispanic*age Hispanic* age2 A/PI*age A/PI* age2 AI/AN*age 2 AI/AN* age

us

cr

Table 4. Odds Ratios and Confidence Intervals for Multilevel Logistic Regression Models of Current Smoking: Age by Role Interactions Females Model 1 Model 2 Model 3 Model 1 OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Fixed Effects Constant 0.34 *** 0.31,0.38 0.34 *** 0.30,0.38 0.34 *** 0.31,0.38 0.29 *** 0.26,0.32

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

1.12 2.94 0.49 2.66 0.96 1.25 1.14 11.13

0.84,1.49 *** 1.78,4.84 *** 0.36,0.67 *** 1.56,4.52 0.58,1.59 0.55,2.85 0.37,3.53 * 1.44,86.25

0.96

0.83,1.11

0.86,1.64

0.85 *

0.80

0.98 0.62,1.54 0.60 *** 0.47,0.75

** *** *** **

* *** *** ***

1.06,1.88 1.77,4.61 0.38,0.70 1.40,3.75 0.90,2.22 0.55,2.28 0.33,2.48 0.61,18.73

** *** *** **

1.11,1.97 1.68,4.39 0.38,0.70 1.36,3.67 0.92,2.29 0.56,2.33 0.34,2.60 0.59,18.18

0.57,1.12 0.71 0.42,1.19 0.53 *** 0.41,0.69

1.04 1.06 1.50 1.77 **

0.76,1.43 0.64,1.75 0.78,2.91 1.18,2.65

Estimate Estimate Estimate Estimate Estimate Random Effects SE SE SE SE SE 3.16 Variance of age 3.09 0.33 3.10 0.33 0.34 3.32 0.35 3.31 0.35 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Variance of age Variance of constant 6.34 0.31 6.33 0.31 6.39 0.31 5.27 0.26 5.25 0.26 *** p < .001; ** p < .01; * p <.05 Source: National Longitudinal Study of Adolescent Health (Female N person =8,740; Female N person-time = 29,437; Male N person=8,199; Male N person-time = 26,661) Notes: Models include covariates as in previous tables. Age, age squared, racial categories, and parent smoking status are centered to their sample means.

37

0.99

0.72,1.35

0.68 1.46 2.07 *

0.38,1.22 0.73,2.92 1.06,4.03

Estimate 3.35 0.00 5.29

SE 0.35 0.00 0.27

Page 39 of 41

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Figure 1. Predicted Probabilities of Smoking by Parents' Education: Females

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Figure

B. With achieved SES and adult roles (Table 3)

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A. No controls for achieved SES or adult roles (Table 2) 1

M an

0.8 0.6 0.4

ed

0.2 0

ce pt

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Black

Hispanic

AI/AN

Ac

White and A/PI

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Page 40 of 41

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B. With achieved SES and adult roles (Table 3)

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A. No controls for achieved SES or adult roles (Table 2)

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Figure 2. Predicted Probabilities of Smoking by Parents' Education: Males

1

M an

0.8

0.6

0.4

ed

0.2

0

ce pt

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Black

Hispanic

AI/AN

Ac

White and A/PI

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Page 41 of 41