Social Influence and Psychological Determinants of ... - CiteSeerX

Social Influence and Psychological Determinants of Smoking Among Inner-City Adolescents Jennifer A. Epstein Gilbert J. Botvin Tracy Diaz

ABSTRACT. Adolescent smoking continues to rise in the United States. Individuals from economically-disadvantaged households appear at high risk for smoking. This study focused on a sample of economically-disadvantaged adolescents attending New York City schools (N = 1875). Longitudinal predictors of smoking from four domains (socio-demographic background information, social influences to smoke, social and personal competence, and individual differences) were tested. Social influences to smoke, from mothers and friends, both predicted smoking one year later. Poor decision-making skills, and low psychological wellbeing also predicted subsequent smoking. Conclusion: These findings support social learning theory (Bandura, 1977) and problem behavior theory (Jessor, 1991). Furthermore, the results suggest that training adolescents to resist social influences to smoke, to problem solve and make sound decisions, and how to cope with psychological distress are among the key components for effective smoking prevention approaches. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: [email protected]] Jennifer A. Epstein, PhD, Gilbert J. Botvin, PhD, and Tracy Diaz, MA are affilliated with the Institute for Prevention Research, Department of Public Health, Cornell University Medical College. Address correspondence to Jennifer A. Epstein, Institute for Prevention Research, Cornell University Medical College, 411 East 69th Street, KB 201, New York, NY 10021 (E-mail: [email protected]). This study was supported by Grant 1 R03 CA 73020 from the National Cancer Institute to Dr. Epstein. Data collection for this study was supported by Grant 1 R18 CA 39280 from the National Cancer Institute to Dr. Botvin. Journal of Child & Adolescent Substance Abuse, Vol. 8(3) 1999 E 1999 by The Haworth Press, Inc. All rights reserved.

1

2

JOURNAL OF CHILD & ADOLESCENT SUBSTANCE ABUSE

Every day 3000 adolescents start smoking in the United States. National survey data from the Monitoring the Future Study indicate that cigarette smoking has significantly increased from 1991 to 1997 among 8th, 10th, and 12th graders for lifetime use, 30-day prevalence and daily use (Johnston, O’Malley & Bachman, in press). For example, from 1991 to 1997 the 30-day prevalence rates climbed from 14.3% to 19.4% among 8th graders, 20.8% to 29.8% among 10th graders, and 28.3% to 36.5% among 12th graders. Furthermore, this rise in adolescent cigarette smoking was apparent for all three ethnic groups surveyed (whites, blacks and Hispanics). These trends will have a major impact on adult smoking in the 21st century because most people begin smoking in adolescence. In fact, five million people aged 17 or under in 1995 are projected to die prematurely due to smoking (CDC, 1996). Moreover, prospective research confirms that adolescent smoking boosts the risk of subsequent smoking in adulthood (Chassin, Presson, Sherman & Edwards, 1990; Chassin, Presson, Rose & Sherman, 1996 ). Adolescents from economically-disadvantaged households may be more prone to smoking than those from higher-income households. This is possibly due to a greater number of role models who smoke and less adult supervision (Perry, Kelder & Komro, 1993). Parental education often serves as a measure of family socio-economic status in research conducted with adolescents. Smoking has consistently shown a negative relationship with higher levels of parental education among youth participating in the Monitoring the Future surveys (Johnston et al., in press). Other studies found a similar relationship between adolescent smoking and parental education (Ary & Biglan, 1988; Mittlemark et al. 1987; Waldron & Lye, 1990). Furthermore, students from a lower-income area school had higher smoking rates than those from a higher-income area school (Semmer, Lippert et al., 1987). These findings suggest that lower income adolescents who are economically-disadvantaged are at risk of becoming smokers. Therefore, this group of adolescents deserves to be the focus of research concerning factors promoting adolescent smoking. Ethnic minority populations have also been identified as being at high risk for smoking (Glynn, Anderson & Schwartz, 1991). In recent years, cigarette advertising and promotions have targeted Hispanic and black populations in this country (Chen, 1993; Moore, Williams & Qualls, 1996). According to the Hispanic Health and Nutrition Ex-

Epstein, Botvin, and Diaz

3

amination Survey (HHANES), Hispanics have higher smoking rates than whites (Haynes, Harvey, Montes, Nickens & Cohen, 1990). Fewer blacks quit smoking and the incidence of cancer and its associated mortality is higher among blacks than whites (USDHHS, 1991). Economically-disadvantaged ethnic minority youth living in inner-city regions may be at great risk for smoking (Oetting & Beauvais, 1990). Yet, little information concerning longitudinal predictors of smoking among such adolescents is available. Social influences to smoke appear to be among the most critical factors in smoking acquisition. Social learning theory emphasizes that people learn behaviors like smoking through modeling, imitation, and reinforcement (Bandura, 1977). Parents’ smoking predicted their children’s smoking longitudinally in several studies conducted with primarily white samples (Chassin et al., 1984; Goddard, 1990; Murray et al., 1983). In cross-sectional research with Hispanic youth, parents’ smoking was related to their children’s smoking (Dusenbury et al., 1992; Dusenbury, Epstein, Botvin & Diaz, 1994). Older siblings’ smoking was a longitudinal predictor in studies with predominantly white samples (Bauman et al., 1984; Chassin et al., 1984; McCaul et al., 1982), with a Hispanic sample (Cowdery, Fitzhugh & Wang, 1997) and a cross-sectional predictor in studies conducted with Hispanic adolescents (Dusenbury et al., 1992; Dusenbury et al., 1994) and black adolescents (Botvin, Baker, Goldberg, Dusenbury & Botvin, 1992). Reviews of the literature indicate that friends’ smoking is a primary and consistent predictor of adolescent smoking (Botvin & Epstein, 1998; Botvin, Epstein & Botvin, 1998). Friends’ smoking has also been shown to be related to smoking among Hispanic and/or black adolescents in cross-sectional research (Botvin et al., 1992; Botvin, Epstein, Schinke & Diaz, 1994; Dusenbury et al., 1992; Dusenbury et al., 1994; Smith, McGraw & Carrillo, 1991). The integration of social learning theory (Bandura, 1977) and problem behavior theory (Jessor & Jessor, 1977) conceptualizes smoking as socially learned, purposeful, and functional behavior resulting from the interplay between social and personal factors. Some adolescents who are not successful academically or socially may begin to smoke as an alternative means of achieving popularity, social status, or selfesteem. These adolescents are most likely deficient in the personal and social skills that would help them master the everyday challenges of this developmental stage. The development of personal and social

4


skills, such as sound decision-making skills and assertive skills, depends upon having opportunities to observe and practice them. Adolescents also need to learn refusal skills. These skills help them resist pressure to engage in problem behaviors like smoking. Efficacy in the ability to use these skills is also critical in developing social and personal competence. Deficiency in refusal skills predicted smoking onset in several prospective studies (Lawrence & Robinson, 1986; Stacy et al., 1988; Sussman et al., 1987; Wills, 1985) and in a crosssectional sample of black adolescents (Botvin et al., 1992). Poor decision-making skills predicted subsequent smoking as well (Wills, 1985). Low self-efficacy has also been linked to adolescent smoking (Ellickson & Hays, 1990-91; Lawrance & Robinson, 1986; Stacy et al., 1992). Individual differences can promote adolescent cigarette smoking. Low self-esteem has been associated with smoking initiation (Ahlgren et al., 1982; Young & Werch, 1990). Low self-esteem predicted intentions to smoke among black adolescents (Botvin et al., 1992) and among a sample of primarily black and Hispanic adolescents (Botvin et al., 1994). Attitudes related to smoking predicted smoking longitudinally among a sample of predominantly white adolescents (Chassin et al., 1984) and cross-sectionally among a sample of black adolescents (Botvin et al., 1992). Psychological well-being or distress has been explored as a predictor of adolescent smoking. Depressed mood was concurrently related to smoking among a predominantly white and Asian sample of 11th graders (Covey & Tam, 1990) and among a Canadian sample of 6th graders (Pederson, Koval & O’Connor, 1997). Feelings of hopelessness concurrently predicted ever use of cigarettes among a predominantly black and Hispanic sample of 7th graders (Botvin et al., 1994). Predictors of adolescent smoking can be divided into four domains: socio-demographic background information (age, sex, academic performance, ethnicity, two-parent household), social influences to smoke (friends’ smoking, peer smoking norms, adult smoking norms, siblings’ smoking, mother’s smoking, father’s smoking), social and personal competence (decision-making skills, assertiveness, efficacy) and individual differences (self-esteem, smoking attitudes, psychological well-being). The purpose of this study is to examine as complete a model as possible of longitudinal predictors of smoking from these four domains among inner-city adolescents. In earlier research we


5

focused on ethnic and gender differences in smoking at each of three time points from the larger investigation (Epstein, Botvin & Diaz, 1998). The current study describes a more complete model of smoking behavior among inner-city youth. Consequently, this study will help identify which factors promote smoking in this understudied population and use this information to determine which strategies are needed for effective prevention for these youth. METHOD Overview A total of 22 middle and junior high schools in New York City with 25% or more Hispanic students participated. These schools were the non-treatment control schools in a longitudinal smoking prevention trial that consisted of a total of 47 schools. (Botvin, Dusenbury et al., 1992). New York City schools serve urban youth from low-income families; the majority of the 22 schools served youth from families with average incomes at or below 150% of the Federal poverty level. All sixth and seventh graders in English-speaking, mainstream classes completed questionnaires. A passive consent procedure was used, and more than 90% of the students completed this initial survey. Schools were targeted for inclusion in the larger longitudinal investigation based on size, location, and student body of at least 25% Hispanic students. Recruitment for the prevention trial was on an ongoing basis until the proper sample size and number schools was obtained. A response deadline was set and the target number of students and schools were reached. Participants At the Year 1 assessment, 2192 students participated. By the Year 2 assessment conducted one year later, there was 86% retention (N = 1875). Overall retention rates compare favorably with retention findings from similar school-based studies (Pentz et al., 1989; Snow et al., 1992). This is particularly impressive considering the recognized difficulty of conducting longitudinal research with inner-city youth due to greater mobility and higher absentee rates. The mean age at Year 1 for

6


the merged Years 1 and 2 sample was 12.8 (SD = .80). The sample was 53% girls and 47% boys. In terms of ethnicity, this sample was 53% Hispanic, 21% black, 15% white and 10% Asian or other. Sixty-nine percent of the respondents lived in two-parent households. Tracking and Minimizing Attrition Individual students were tracked by means of a master list and unique ID codes, permitting linkage of data from different time points. This system involved: (1) obtaining class lists from all schools in the study, (2) assigning unique ID numbers for each student, (3) printing op-scan questionnaires pre-lithocoded with individual ID codes, and (4) preparing lists of computer generated name labels. To facilitate distribution and to ensure that students received the correct questionnaire, the student’s name label was affixed to the student’s data collection packet. Project staff collected the empty packets with the student’s name label and discarded them while the students completed the questionnaire preserving students’ confidentiality. This ID coding system decreased sample attrition due to incomplete or inaccurate ID codes. To retain the largest number of students possible, students were tracked using: ID information obtained at the beginning of the study; information collected on registration forms updating names of parents and relatives, addresses, phone numbers and where they planned to go to school next year; and school records. Student information was updated throughout the project using a computer database. To minimize attrition and maximize retention: (1) careful records of the location of all student participants were kept and (2) attrition due to absenteeism was minimized by pursuing absentees on at least one return data collection to participating schools. Procedure All participating students completed questionnaires at each assessment that measured self-reported smoking and cognitive, attitudinal, and psychological characteristics hypothesized to be related to smoking initiation. A team of three to five data collectors, who were members of the same ethnic groups as the participating students, administered the surveys following the same standardized protocol at each assessment. Students completed these surveys during a regular


7

40-minute class period over a four-week interval. Teachers were not involved in data collection activities; students were assured that their answers would remain confidential. Quality of self-report data was assured by: (1) the use of student identification codes rather than names to emphasize the confidential nature of the surveys, and (2) the collection of carbon monoxide breath samples before students completed the questionnaire. Smoking at this age tends to be too episodic to accurately validate by objective measures. Nevertheless, collecting carbon monoxide breath samples does enhance the veracity of self-reported smoking data in adolescents (Bauman, Koch & Bryan, 1988; Evans, Hansen & Mittlemark, 1977; Murray, O’Connell, Schmid & Perry, 1987). Training for data collectors included a systematic means of collecting carbon monoxide samples. At the same time that the students completed the questionnaires, carbon monoxide samples were collected in a procedure that lasts less than one minute per student. Overview of Measures Students completed one of two randomly distributed questionnaire forms containing the same items with the order reversed for the measures on the last half of the questionnaire. Half the sample completed each form, thereby maximizing the amount of data collected within the available time and minimizing data loss due to fatigue, boredom, or inadequate time. All data on the behavioral and psychosocial measures were self-report. Included on the questionnaires were items concerning ethnicity, gender, and age; items assessing the smoking behavior of respondents and their significant others (parents, older siblings, and friends); and items assessing smoking attitudes and normative beliefs, self-efficacy, assertiveness, decision-making skills, self-esteem, and psychological well-being. All of the items/scales used were derived from psychometrically-valid and widely used instruments. However, since the items used to measure several of these variables had originally been developed for use with white, middle-class students, they were pilot-tested and revised where necessary to ensure their suitability for the target population (Botvin, Dusenbury et al., 1989). These measures were then used in a large-scale smoking prevention trial from which the data for the current study were drawn; the alphas reported below are from this larger sample (Botvin, Dusenbury et al., 1992).

8


Cigarette Smoking An 11-point smoking index assessed smoking frequency. Specifically, students responded to the question, ‘‘How often do you currently smoke?’’ Response options included: ‘‘ I have never smoked’’ (1); ‘‘Not at all in the last 12 months’’ (2); ‘‘Once or twice in the last 12 months’’ (3); ‘‘A few times in the last 12 months’’ (4); ‘‘Usually once a month’’ (5); ‘‘A few times each month’’ (6); ‘‘Usually once a week’’ (7); ‘‘A few times each week’’ (8); ‘‘A few times most days’’ (9); ‘‘About half a pack each day’’ (10); and ‘‘A pack or more each day’’ (11). This continuous smoking index serves as the dependent variable in the current study. Demographic Variables Students identified themselves as members of ethnic groups by their responses to two questions: (1) ‘‘Choose the category which best describes you’’ and (2) ‘‘If you are Latino/Hispanic, choose the group that best describes you.’’ For the first question, the response categories were Black, White, Latino/Hispanic, Asian, and other. The response categories for the Hispanic subgroup question were Puerto Rican, Dominican, Cuban, Colombian, Mexican, Ecuadorian, Other Hispanic, Latino/Hispanic combinations. When respondents indicated they were a member of a Hispanic subgroup on the second question, then we classified them as Hispanic even if they had not indicated their Hispanic status on the first question. Students indicated whether they were male or female. Then they indicated whom they lived with most of the time from which it was determined if they lived in a two-parent household or not. Respondents reported when they were born which allowed a calculation of their age. Social Influence Variables Respondents indicated whether their mother, their father, and their sibling(s) smoked. Friends’ smoking was assessed with a five-point scale ranging from ‘‘none’’ (1) to ‘‘all or nearly all’’ (5). Two items measured respondents’ normative expectations concerning cigarette smoking (‘‘In your opinion, how many adults [people your age] smoke cigarettes?’’) and were rated on 6-point scales ranging from ‘‘none’’ (1) to ‘‘almost all’’ (6).


9

Social and Personal Competence Variables Decision-making. Seven items ( = .82) assessed decision-making skills. This measure was derived from the nine-item subscale of the Coping Inventory (Wills, 1986) concerning problem-solving and direct action. The seven items were selected by including items with factor loadings of .50 or higher. The decision-making measure assessed the use of sound decision-making skills (e.g., ‘‘when I have a problem I get information that is needed to deal with the problem’’). Responses were rated on a 5-point scale which ranged from ‘‘never’’ (1) to ‘‘almost always’’ (5). Assertiveness. Assertiveness was assessed using an 18-item scale ( = .72) derived from the Assertion Inventory (Gambrill & Richey, 1975). Validity for this instrument has been previously established by showing expected relationships between clinical and non-clinical samples as well as significant correlations with the ratings of blind observers concerning the level of assertiveness demonstrated in role play situations. Responses were rated on a 5-point Likert scale which ranged from ‘‘never’’ (1) to ‘‘almost always’’ (5). Examples of assertive behaviors include returning defective merchandise, complaining when someone steps ahead in line, and saying ‘‘no’’ in various situations. Self-efficacy. Self-efficacy was assessed using five items from the personal efficacy subscale of the Spheres of Control Scale (Paulhus, 1983). This scale measured the extent to which respondents believed they could achieve personal goals through their own efforts (e.g., ‘‘I can learn almost anything if I set my mind to it’’). It has been found to have test-retest reliabilities of greater than .90 at 4 weeks. The reliability of the shortened 5-item scale used in this study was reasonably good ( = .75). Responses were scored on a 5-point Likert scales which ranged from ‘‘strongly disagree’’ (1) to ‘‘strongly agree’’ (5). Individual Difference Variables Smoking attitudes. Attitudes about smoking, smokers, and the perceived benefits of smoking (e.g., ‘‘smoking cigarettes makes you look cool’’) were measured using 10 items ( = .69) derived from an item-analysis of the Teenager’s Self Test: Cigarette Smoking (USPHS, 1974). Responses for each item were scored using a 5-point Likert

10


scale which ranged from ‘‘strongly disagree’’ (1) to ‘‘strongly agree’’ (5). Individual items were summed for a total score, with high scores indicating anti-smoking attitudes. Self-esteem. Self-esteem ( = .80) was measured by the 10-item scale developed by Rosenberg (1965). Statements were typical evocations of self-esteem (e.g., ‘‘I take a positive attitude toward myself,’’ ‘‘I feel that I have a number of good qualities’’). Responses were scored on a 5-point Likert scale which ranged from ‘‘strongly disagree’’ (1) to ‘‘strongly agree’’ (5). Psychological well-being. Psychological well-being was assessed using an empirically reduced version of the mental health inventory scale developed by Veit and Ware (1983). Items were selected and judged to be valid by a panel of four psychologists. These items were then pilot-tested and factor analyzed. Items with factor loadings of .50 or greater were selected for inclusion in the final 12-item scale ( = .80). Items consisted of self-statements about the frequency of feeling rested, happy, and relaxed in the last month. Responses were rated on a 5-point Likert scale which ranged from ‘‘none of the time’’ (1) to ‘‘most of the time’’ (5). RESULTS Overview Only individuals providing data at both the Year 1 and Year 2 assessments were included in the analyses reported below. First, the sample used in this study was examined to determine the impact of attrition. Then, a series of multiple regression analyses were conducted to determine which of the variables predicted smoking longitudinally. Attrition Analyses Analyses were conducted to appraise the extent to which any potential bias resulting from differential attrition might have been introduced into the study. To determine the extent of attrition bias, a one-way ANOVA using Year 1 Smoking Index dichotomized into never smoked (response 1 on the 11-point index) versus ever smoked


11

(the remaining 10 categories on the 11-point index) as an independent factor was conducted with retention rate as the dependent variables. According to this analyses, the retention rate was higher for students who never smoked (86%) than students who had ever smoked (83%), F(1, 2166) = 4.12, p < .05. Although this difference was significant, the retention rates were not that far apart (3% difference). This loss of a slightly higher proportion of students who had ever smoked at Year 1 due to attrition (absenteeism and relocation) only minimally disrupts the estimation of variable relationships. Longitudinal Predictors of Smoking Overview of data analyses. Four sets of regression analyses were conducted to assess which of the variables were the most important predictors of one-year follow-up smoking among these multi-ethnic adolescents. Individuals for whom any of the variables in the equation was missing were omitted from the analysis. Four regression equations were computed corresponding to the variable domains included in the study. Each regression included the earlier version of the smoking index from the prior year to control for smoking in Year 1. The first equation represented demographics: gender, age, academic performance, family composition (not two-parent household, two-parent household), and ethnicity (dummy-coded with black adolescents as the referent for Hispanic adolescents, white adolescents, and adolescents from other ethnic groups). The second regression featured the social influences to smoke: friends, mothers, fathers, siblings, peer norms and adult norms. The third equation consisted of the social and personal competence skills: decision-making, assertiveness, and selfefficacy. Finally, the last regression was composed of relevant individual characteristics: anti-smoking attitudes, well-being, and self-esteem. The significant predictors from each of the four regressions were included in a final regression model. Table 1 shows which variables from the four regressions were significant and tested in the final model. This overall model was significant [F(7,1571) = 127.93, p < .001] and nearly all the individual predictors remained significant. In addition, this model accounted for 36% of the variance. Earlier smoking was associated with subsequent smoking , t(1571) = 23.30, p < .001. Being older increased subsequent frequency of smoking somewhat, t(1571) = 1.84, p = .07. Two social influences, mother’s smoking, t(1571) = 2.91, p < .01, and friends’


12

TABLE 1. Predictors of Longitudinal Smoking Final Regression Model Variable Smoking from Prior Year Age Sex Mother’s Smoking Friends’ Smoking Decision-Making Well-Being

Beta .52 .04 .02 .06 .10 .04 .05

t

p

23.30 1.84 1.05 2.91 4.35 2.06 2.12

.001 .067 .296 .004 .001 .039 .034

smoking, t(1571) = 4.35, p < .001), both predicted smoking one year later. Poor decision-making skills predicted subsequent smoking, t(1571) = 2.06, p < .05. Finally, low psychological well-being predicted smoking one year later, t(1571) = 2.12, p < .05. DISCUSSION Based on the findings from this longitudinal study, both social learning (Bandura, 1977) and problem behavior theory (Jessor, 1991) received support. Social influences to smoke, deficiencies in personal competence and individual differences were implicated in adolescent smoking among a large sample of economically-disadvantaged innercity adolescents. This is important because economically-disadvantaged adolescents remain most at risk of smoking (Ary & Biglan, 1988; Mittlemark et al., 1987; Perry et al., 1993; Semmer et al., 1987; Waldon & Lye, 1990). Furthermore, inner-city ethnic minority adolescents have often been overlooked in longitudinal research pertaining to the etiology of smoking. The current study indicated that being older had some relationship with subsequent smoking. Social influences to smoke influenced respondents’ smoking one year later. Specifically, mother’s smoking and friends’ smoking measured in the first year both predicted students’ smoking measured in the second year. A deficiency in decision-making skills served as a longitudinal predictor of smoking. Low psychological well-being (or alternatively high psychological distress) was associated with subsequent smoking. There was no evidence of gender or ethnic differences in smoking


13

when other variables were included in the models tested in this study (cf. Epstein et al., in press). This is noteworthy because the sample size of the current study was relatively large. An earlier cross-sectional study of black inner-city adolescents also failed to find gender differences (Botvin et al., 1992), and another cross-sectional study of predominantly black and Hispanic inner-city adolescents did not demonstrate gender or ethnic differences in smoking (Botvin et al., 1994), but those samples were much smaller in size. In terms of gender, these findings are consistent with the national Monitoring the Future survey results for 8th graders, but gender differences may develop at a later age (Johnston et al., in press). Prevalence surveys have found lower rates of smoking for black adolescents relative to white and Hispanic adolescents (Johnston et al., in press; Kann et al., 1996). Ethnic differences in smoking simply may not have emerged as early as sixth or seventh grade. Alternatively, other factors in our etiological models may control for ethnic differences and if the same factors were controlled in surveys of national prevalence rates then perhaps those ethnic differences would disappear also. The relationship between being older and smoking is congruous with past survey research (Johnston et al., in press; Kann et al., 1996). Among social influences to smoke from family members, mother’s smoking predicted respondents’ smoking one year later. The results concerning mother’s smoking is consistent with prior cross-sectional research conducted with Hispanic adolescents (Dusenbury et al., 1992; Dusenbury et al., 1994) and prior longitudinal research conducted with predominantly white samples (Chassin et al., 1984; Goddard, 1990; Murray et al., 1983). In contrast with earlier work, the current study failed to find that father’s smoking had an impact on adolescent smoking. Sibling’s smoking also was not associated with respondents’ later smoking, contrary to earlier research with Hispanic adolescents cross-sectionally (Dusenbury et al., 1992; Dusenbury et al., 1994) and with predominantly white samples longitudinally (Bauman et al., 1984; Chassin et al., 1984; McCaul et al., 1982). Father’s and sibling’s smoking may be less important in predicting smoking long term among a sample of primarily minority adolescents. Mothers may simply be the key family role model, as they were present in nearly every family, unlike fathers or siblings. In addition, even the social influence to smoke from mothers could diminish over time.

14


Family members may be less important social influences compared to friends as adolescents grow older. Past cross-sectional studies indicated that friends’ smoking was associated with smoking among Hispanic and/or black adolescents (Botvin et al., 1992; Botvin, Epstein, Schinke & Diaz, 1994; Dusenbury et al., 1992; Dusenbury et al., 1994; Smith, McGraw & Carrillo, 1991). However, in cross-sectional research, it is impossible to determine if the smoking behavior of friends causes adolescents to smoke or if adolescents who smoke seek out other smokers as friends. Prior longitudinal research conducted with white adolescents showed the pivotal role of friends who smoke (Botvin & Epstein, 1998; Botvin, Epstein & Botvin, 1998). Since adolescents of all ethnic groups spend much of their time with their friends, it is not surprising that the current study found that friends were a crucial social influence to smoke. In contrast, perceptions that ‘‘everyone is smoking’’ among peers and adults did not predict subsequent smoking in this predominantly minority adolescent sample. Evidence of a relationship between peer smoking norms and concurrent smoking among inner-city minority youth has been mixed with one study finding a relationship (Botvin et al., 1994) and others not finding one (Botvin et al., 1992; Dusenbury et al., 1992). Future etiologic studies should continue to test a variety of social influences. Moreover, smoking prevention approaches ought to continue to provide students with an awareness of the various social influences to smoke (particularly regarding friends) and skills to resist these social influences to smoke. Poor decision-making skills predicted later smoking. The deficiency in this personal skill may have been responsible for the decision to smoke. Adolescents who have not been able to observe and practice sound decision-making skills are at a distinct disadvantage when faced with social influences to smoke from family, friends, and the media. Competence enhancement approaches to smoking prevention typically teach general problem-solving and decision-making skills (Botvin & Epstein, 1998; Botvin, Epstein & Botvin, 1998). Prevention studies conducted with inner-city Hispanic youth (Botvin, Dusenbury, Baker, James-Ortiz & Kerner, 1989; Botvin, Dusenbury et al., 1992) and African-American youth (Botvin et al., 1989; Botvin & Cardwell, 1992) have demonstrated that such a competence enhancement approach can decrease cigarette smoking relative to a control group in the seventh grade. The finding of the current study suggests that en-


15

hancing decision-making skills is a critical component in preventing smoking among inner-city minority adolescents. Being psychologically distressed predicted subsequent smoking. This extends findings from cross-sectional studies investigating the link between depressed mood and smoking among white and Asian adolescents (Covey & Tam, 1990) and Canadian adolescents (Pederson et al., 1997). Competence enhancement approaches to smoking prevention usually have a component on coping with anxiety (Botvin & Epstein, 1998; Botvin, Epstein & Botvin, 1998). Thus the link between psychological distress and smoking in this study implies that inclusion of training in coping with anxiety is important. Yet, ways of combating other aspects of psychological distress, such as depressed mood, need to be incorporated as well. Furthermore, the process of adjusting to a new culture known as acculturation may create psychological distress among Hispanic adolescents (Austin & Gilbert, 1989). In fact, acculturation has been linked with adolescent smoking among Hispanics (Dusenbury et al., 1994; Epstein, Botvin & Diaz, 1998; Smith et al., 1991). Several limitations of this study need to be noted. As our sample was school-based, caution is warranted in generalizing the findings from this study. Yet, this study was conducted in the middle school period when dropout rates remain low and absentee data were minimized by pursuing absentees on two return data collections. Since this study focused on students living in New York City, it is possible that the results could differ from other urban areas. Yet, additional research conducted in other inner-city regions most likely would confirm the generalizability of the theoretical foundation specified here. Attrition analyses indicated the loss of a slightly higher proportion of ever smokers, which minimally disrupted the estimation of variable relationships. These findings have important implications for smoking prevention for this population. Four of the predictors identified here are factors that can be addressed in an intervention. Both mother’s smoking and friends’ smoking fall under the domain of providing students with an awareness of various social influences to smoke and smoking-specific refusal skills to resist these influences. The generic social and personal skills taught in competence enhancement approaches to smoking prevention should help adolescents cope with these social influences to smoke as well. Morever, this study suggests that training in sound decision-making skills ought to decrease levels of adolescent smok-

16


ing. Finally, smoking prevention programs need to include methods of coping with psychological distress. REFERENCES Ahlgren, A., Norem, A.A., Hochhauser, M. & Garvin, J. (1982). Antecedents of smoking among pre-adolescents. Journal of Drug Education, 12, 325-340. Ary, D.V. & Biglan, A. (1988). Longitudinal changes in adolescent cigarette smoking behavior: Onset and cessation. Journal of Behavioral Medicine, 11, 361-382. Austin, G.A. & Gilbert, M.J. (1989). Substance abuse among Latino youth. Prevention Research Update, 3, 1-28. Bandura, A. (1977). Social learning theory. Englewood Cliffs, New Jersey: Prentice Hall. Bauman, K.E., Fisher, L.A., Bryan, E.S. et al. (1984). Antecedents, subjective utility, and behavior: A panel study of adolescent cigarette smoking. Addictive Behaviors, 15, 121-136. Bauman, K.E., Koch, G.G. & Bryan, E.S. (1988). Validity of self-reports of adolescent cigarette smoking. International Journal of the Addictions, 17, 1131-1136. Botvin, G.J., Baker, E., Goldberg, C.J., Dusenbury, L. & Botvin, E.M. (1992). Correlates and predictors of smoking among black adolescents. Addictive Behaviors, 17, 97-103. Botvin, G.J., Batson, H.W., Witts-Vitale, S., Bess, V., Baker, E. & Dusenbury, L. (1989). A psychosocial approach to smoking prevention for urban black youth. Public Health Report, 104, 573-582. Botvin, G.J., Dusenbury, L., Baker, E., James-Ortiz, S., Botvin, E.M. & Kerner, J. (1992). Smoking prevention among urban minority youth: Assessing effects on outcome and mediating variables. Health Psychology, 11, 290-299. Botvin, G.J., Dusenbury, L., Baker, E., James-Ortiz, S. & Kerner, J. (1989). A skills training approach to smoking prevention among Hispanic youth. Journal of Behavioral Medicine, 12, 279-296. Botvin, G.J. & Epstein, J.A. (1998). Preventing cigarette smoking among children and adolescents. In D.F. Seidman & L. Covey (Eds.), Helping smokers quit: A handbook for the helping professions. Mahwah, N.J: Lawrence Erlbaum Associates. Botvin, G.J., Epstein, J.A. & Botvin, E.M. (1998). Adolescent cigarette smoking: Prevalence, causes and intervention approaches. S.B. Friedman & D. DeMaso (Eds.), Adolescent Medicine:State of the Art Reviews. Philadelphia, PA: Hanley & Belfus, Inc. Botvin, G.J., Epstein, J.A., Schinke, S.P. & Diaz, T. (1994). Predictors of smoking among inner-city minority youth. Journal of Deviant Behavioral Pediatrics, 15, 67-73, 1994. Centers for Disease Control and Prevention. (1996). Projected smoking-related deaths among youth- United States. Mortality and Morbidity Weekly Reports, 45, 971-974. Chassin, L., Presson, C.C., Rose, J. & Sherman, S.J. (1996). The natural history of


17

cigarette smoking from adolescence to adulthood: Demographic predictors of continuity and change. Health Psychology, 15, 478-484. Chassin, L.A., Presson C.C, & Sherman, S.J. (1984). Cigarette smoking and adolescent psychosocial development. Basic and Applied Social Psychology, 99, 722-42. Chassin, L., Presson, C.C. Sherman, S.J. & Edwards, D. (1990). The natural history of cigarette smoking: Predicting young-adult outcomes from adolescent smoking patterns. Health Psychology, 9, 701-716. Chen, V.W. (1993). Smoking and the health gap in minorities. Annual Epidemiology, 3, 159-164, 1993. Covey, L.S. & Tam, D. (1990). Depressive mood, the single-parent home, and adolescent smoking behavior. American Journal of Public Health, 80, 1330-1333. Cowdery, J.E., Fitzhugh, E.C. & Wang, M.Q. (1997). Sociobehavioral influences on smoking initiation of Hispanic adolescents. Journal of Adolescent Health, 20, 46-50. Dusenbury, L., Epstein, J.A., Botvin, G.J. & Diaz, T. (1994). The relationship between language spoken and smoking among Hispanic-Latino youth in New York City. Public Health Reports, 109, 421-427. Dusenbury, L., Kerner, J.F., Baker, E., Botvin, G.J., James-Ortiz, S. & Zauber, A. (1992). Predictors of smoking prevalence among NewYork Latino youth. American Journal of Public Health, 82, 55-58. Ellickson, P.L. & Hays, R.D. (1990). Beliefs about resistance self-efficacy and drug prevalence: Do they really affect drug use? International Journal of Addiction, 25, 1353-1378. Epstein, J.A., Botvin, G.J. & Diaz, T. (in press). Ethnic and gender differences in smoking prevalence among a longitudinal study of inner-city adolescents. Journal of Adolescent Health. Epstein, J.A., Botvin, G.J. & Diaz, T. (1998). Linguistic acculturation and gender effects on smoking among Hispanic youth. Preventive Medicine, 27, 583-589. Evans, R.I., Hansen, W.B. & Mittlemark, M.B. (1977). Increasing the validity of self-reports of smoking behavior in children. Journal of Applied Psychology, 62, 521-523. Gambrill, E.D. & Richey, C.A. (1975). An assertion inventory for use in assessment and research. Behavior Therapy, 6, 550-561. Glynn, T.J., Anderson, D.M. & Schwartz, L. (1991). Tobacco-use reduction among high-risk youth: Recommendations of a National Cancer Institute advisory panel. Preventive Medicine, 20, 279-291. Goddard, E. (1990). Why children start smoking. London: Her Majesty’s Stationery Office. Haynes, S. G., Harvey, C., Montes, H. et al. (1990). Patterns of cigarette smoking among Hispanics in the United States: Results from HHANES 1982-84. American Journal of Public Health, 80, 47-53. Jessor, R. & Jessor, S.L. (1977). Problem behavior and psychosocial development: A longitudinal study of youth. New York: Academic Press. Johnston, L.D., O’Malley, P.M. & Bachman, J.G. (in press). National survey results

18


on drug use from the Monitoring the Future study, 1975-1997, Volume I Secondary School Students. Rockville, MD: National Institute of Drug Abuse. Kann, L., Warren, C.W., Harris, W.A. et al. (1996).Youth risk behavior surveillance-United States, 1995. CDC Surveillance Summaries, September 27, 1996. Mortality and Morbidity Weekly Report, 45(No. SS-4), 1-26. Lawrance, L. & Rubinson, L. (1986). Self-efficacy as a predictor of smoking behavior in young adolescents. Addictive Behaviors, 11, 367-382. McCaul, K.D., Glasgow, R., O’Neil, L.H.K. et al. (1982). Predicting adolescent smoking. Journal of School Health, 52, 342-346. Mittlemark, M.B., Murray, D.M., Luepker, R.V. et al. (1987). Predicting experimentation with cigarettes: The childhood antecedents of smoking study. American Journal of Public Health, 77, 206-208. Moore, D.J., Williams, J.D., Qualls, W.J. (1996). Target marketing of tobacco and alcohol-related products to ethnic minority groups in the United States. Ethnicity & Disease, 6, 3-98. Murray, D. M., O’Connell, D. M., Schmid, L. A. & Perry, C. L. (1987). The validity of smoking self-reports by adolescents: A reexamination of the bogus pipeline procedure. Addictive Behaviors, 12, 7-15. Murray, D.M., Swan, A.V., Bewley, B.R. et al. (1983). The development of smoking during adolescence--The MRC/Derbyshire smoking study. International Journal of Epidemiology, 12, 185-192. Oetting ER & Beauvais F. (1990). Adolescent drug use: Findings of national and local surveys. Journal of Consulting and Clinical Psychology, 58, 385-394. Paulhus, D. (1983). Sphere-specific measures of perceived control. Journal of Personality and Social Psychology, 44, 1253-1265. Pederson, L.l, Koval, J.J. & O’Connor, K. (1997). Are psychosocial factors related to smoking in grade 6 students? Addictive Behaviors, 22, 169-181. Pentz, M.A., MacKinnon, D. P. & Flay, B.R. et al. (1989). Primary prevention of chronic diseases in adolescence: Effects of the midwestern prevention project on tobacco use. American Journal of Epidemiology, 130, 713-724. Perry, C.L., Kelder, S.H. & Komro, K.A. (1993). The social world of adolescents: Family, peers, schools, and the community. Millstein, S.G. (Ed), Petersen, A.C. (Ed) et al. Promoting the health of adolescents: New directions for the twenty-first century. New York, NY, USA: Oxford University Press. xiv, 403, pp. 73-96. Rosenberg, M. (1965). Society and the Adolescent Self-Image, Princeton, NJ: Princeton University Press. Semmer, N.K., Lippert, P., Fuchs, R. et al. (1987). Adolescent smoking from a functional perspective: the Berlin-Bremen study. European Journal of Psychology Education, 2, 387-401. Smith, K.W., McGraw, S.A. & Carrillo, J.E. (1991). Factors affecting cigarette smoking and intention to smoke among Puerto Rican-American high school students. Hispanic Journal of Behavioral Science, 13, 401-411. Snow, D.L. & Tebes, J.K. (1992). Panel attrition and external validity in adolescent substance use research. Journal of Consulting and Clinical Psychology, 60, 804-807 . Sussman, S., Dent, C.W., Flay, B.R. et al. (1987). Psychosocial predictors of cigarette


19

smoking onset by white, black, Hispanic, and Asian adolescents in Southern California. Mortality and Morbidity Weekly Report, 36(4 Suppl), 11S-17S. United States Department of Health and Human Services [USDHHS]. (1991). National household survey on drug abuse: Population estimates 1990, Washington, DC: National Institute on Drug Abuse DHHS Pub. No. (ADM) 91-1732. U.S. Public Health Service. (1974). Teenager’s self-test: Cigarette smoking, Washington, DC: Centers for Disease Control: U.S. Public Health Service (DHEW Publication No. CDC 74-8723). Veit, C.T. & Ware, J.E. (1983). The structure of psychological distress and well-being in general populations. Journal of Consulting & Clinical Psychology, 51, 730-742. Waldron, I. & Lye, D. (1990). Relationships of teenage smoking to educational aspirations and parents’ education. Journal of Substance Abuse, 2, 201-215. Wills, T.A. (1985). Stress, coping, and tobacco and alcohol use in early adolescence. Shiffman, S. & Wills, T.A. (Eds): Coping and Substance Use. San Diego, Academic Press, 67-94. Wills, T.A. (1986). Stress and coping in early adolescence: Relationships to substance use in urban school samples. Health Psychology, 5, 503-529. Young, M., & Werch, C.E. (1990). Relationship between self-esteem and substance use among students in fourth through twelfth grade. Wellness Perspectives: Research, Theory and Practice, 7, 31-44.

HAWORTH JOURNALS ARE AVAILABLE ON MICROFORM All Haworth journals are now available in either microfiche or microfilm from The Haworth Press, Inc. Microfiche and microfilms are available at the ‘‘library’’ subscription rate. Library hard-copy subscribers may receive microform at $35 per volume. For all microform subscriptions, these charges apply: outside US and Canada: 40% to total; in Canada, 30% to total as well as 7% GST. Microfilm specifications: 35mm; diazo or silver. Microfiche specifications: 105mm x 184mm (4” x 6”); reduction ratio: 24X; nonsilver (diazo) positive polarity. Microform are mailed upon completion of each volume. For further information, contact Janette Kemmerer, Microform Contact, The Haworth Press, Inc., 10 Alice Street, Binghamton, NY 13904-1580; Tel: (607) 722-5857 (ext. 311); Fax: (607) 722-1424; E-Mail: [email protected]

20


ALL HAWORTH BOOKS AND JOURNALS ARE PRINTED ON CERTIFIED ACID-FREE PAPER