- Email: [email protected]
Social distance and homophily in adolescent smoking initiation
Drug and Alcohol Dependence 124 (2012) 347–354
Contents lists available at SciVerse ScienceDirect
Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep
Social distance and homophily in adolescent smoking initiation Myong-Hyun Go a,∗ , Joan S. Tucker b , Harold D. Green Jr. b , Michael Pollard b , David Kennedy b a b
UCLA Center for Biomedical Modeling, United States RAND Corporation, Santa Monica, CA, United States
a r t i c l e
i n f o
Article history: Received 30 September 2011 Received in revised form 2 February 2012 Accepted 7 February 2012 Available online 12 March 2012 Keywords: Adolescent Smoking Influence Selection Longitudinal Social network Propensity score
a b s t r a c t Background: Studies often demonstrate homophily in adolescent smoking behavior, but rarely investigate the extent to which this is due to the peer network processes of selection versus influence. Applying the concept of social distance, this study examines these two processes for smoking initiation. Methods: We analyzed socio-centric network data from the National Longitudinal Study of Adolescent Health (N = 2065; grades = 7th–12th). Social distance (degrees of separation), combined with stability and change in friendship networks, was used to derive indicators of peer selection and influence on initiation. Multilevel modeling was used to predict initiation from these indicators, and propensity score modeling was used to determine whether these associations remained after adjusting for pre-existing differences between initiators and consistent non-smokers. Results: We found that both peer influence and selection effects increased the likelihood of initiation even after adjusting with propensity score weights and demographic controls. While the effect size for peer influence depended on the overall proportion of smokers at the school, the selection effect was independent of the school environment. De-selection and indirect influence effects were not significant after controlling for school norm interactions. Conclusions: The association between peer smoking and adolescent smoking initiation appears to be due to both peer selection and direct influence. However, “friends of friends” effects are likely to be confounded with contextual factors. Given that smoking initiation is primarily associated with close personal interactions between the adolescent and his/her friends, prevention efforts should focus on the role of smoking in fostering personal relationships among adolescents. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction 1.1. Homophily in adolescent smoking behavior Peer smoking is one of the strongest and most consistent predictors of adolescent smoking (Kobus, 2003). However, the nature of this association is not well understood. Studies reporting a correlation between the smoking behavior of adolescents and their peers typically infer that this association is the result of a peer influence effect, ignoring the possibility that adolescents may choose friends who are similar to themselves in terms of smoking. Influence involves an individual’s attributes changing in response to the predominant behavior in his/her social network. Selection involves structural changes in an individual’s social network as the result of the individual selecting to form or dissolve friendships with peers who hold certain attributes; for example, an adolescent may choose
∗ Corresponding author at: UCLA Center for Biomedical Modeling, 10940 Wilshire Blvd Suite 1450, Los Angeles, CA 90024, United States. E-mail address: [email protected] (M.-H. Go). 0376-8716/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2012.02.007
to form friendships with other smokers, or sever ties with peers who smoke. Both influence and selection effects are processes that lead to the homogeneity of peer networks. Adolescents tend to associate with peers who have similar attributes as their own such as age and socioeconomic status (Mariano and Harton, 2005). In addition, adolescents exercise peer pressure on others, which accelerates the process of assimilation (Dishion et al., 1999). Since peer influence and selection effects are both processes of network homogenization rather than its outcomes, the two are indistinguishable if one ignores pathways of network evolution and levels of network homogeneity over time (Alexander et al., 2001; Hall and Valente, 2007; Kirke, 2004). As a result, any identification strategy has to involve at the minimum some form of beforeand-after longitudinal approach to differentiate the two effects (Go et al., 2010; Urberg et al., 1997; Snijders and Pearson, 2010). The longitudinal framework by itself, however, is not sufficient for resolving the question of whether peer selection precedes smoking initiation or the other way around. That is, smoking initiation can bring new friends, but new friendships can also result in smoking initiation. Without “high resolution” data that records minute moments of behavioral changes, one cannot infer from the data the
348
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Table 1 Descriptive statistics for main study variables. Variables
Minimum
Peer network variables Indicators of peer selection 0 # new smoking friends initially >3 degrees away 0 # new smoking friends initially 3 degrees away 0 # new smoking friends initially 2 degrees away Indicators of peer de-selection # former smoking 0 friends now >3 degrees away # former smoking 0 friends now 3 degrees away 0 # former smoking friends now 2 degrees away Indicators of peer influence 0 # consistent smoking friends 1 degree away (direct influence) 0 # consistent smoking peers 2 degrees away (indirect influence) 0 # consistent smoking peers 3 degrees away (indirect influence) 1 # nominated peers at Wave I % consistent 22% non-smokers at the school at Wave I Personal characteristics at Wave I 0 Depression (higher = worse) 1 Grade point average (higher = better) 0 Self-esteem (higher = better) 0 School attachment (higher = better) 0 Delinquency (higher = worse)
Median
Mean
Maximum
0
0.20
6
0
0.05
3
0
0.06
2
0
0.24
6
0
0.03
3
0
0.04
5
0
0.17
6
0
0.20
6
0
0.35
12
2
2.39
10
50%
56%
100%
3
3.41
15
3
2.89
4
4
4.07
5
3
2.85
4
0.13
0.25
3
Variables
%
Female Grade in school at Wave I 7th 8th 9th 10th 11th 12th Race/ethnicity White African American Hispanic Asian Parent education at Wave I Did not finish high school High school graduate/GED Some college College degree or higher Parental smoking at Wave I Mother smokes Father smokes
50.5 11.0 11.3 10.9 31.5 28.7 6.6 57.4 17.9 14.2 10.5 12.0 32.3 22.3 33.4 36.5 42.3
temporal order of smoking initiation vis-à-vis peer selection. However, we propose an innovative approach based on the concept of “social distance” (i.e., degrees of separation) that controls for the autocorrelation of behaviors in the friendship networks (Mercken et al., 2005) and provides a more stringent test of peer influence and selection effects.
Fig. 1. Social space (degrees of separation).
1.2. Differentiating peer influence from selection: the role of social distance Selection is best expressed when an adolescent chooses a peer with similar attributes or behaviors as his or her own. If selection on smoking is a significant driver of smoking homophily, statistical analysis will show that an adolescent smoker is more likely to choose smoking peers as friends rather than the non-smoking peers. Similarly for peer influence, one would observe that the smoking behavior of the focal adolescent synchronizes over time with the predominant smoking behavior of the adolescent’s peers. For example, exposure to smoking peers in terms of both quantity (the number of smoking peers) and duration (e.g., one year) should increase the likelihood of smoking uptake compared to being exposed to non-smoking peers. The task of differentiating selection and influence effects further requires controlling for the problem of autocorrelation, which percolates through the entire network and makes it difficult to properly attribute the source of influence to the immediate peers (Calvó-Armengol et al., 2005; Snijders, 2001). Our approach to addressing the problem of identifying peer influence and selection effects starts with the degrees of separation (i.e., “social distance”) in the adolescent’s social space (Fig. 1). The strength of influence on the focal adolescent by a peer should be proportional to the social distance between the two individuals. For example, friends, who are one degree away, should exert more influence on the adolescent’s behavior than “friends of friends” who are two degrees away. In the case of peer selection, the effect would be clearest when the adolescent is willing to bear the “high cost” of initiating friendship with a socially distant smoker. When an adolescent is willing to befriend a distant smoker (in terms of degree of separation), it is likely that the effect is due to selection without being “contaminated” by the influence effect or some other random factor. Studies by Christakis, Fowler and colleagues (Christakis and Fowler, 2007, 2008; Rosenquist et al., 2010) provide information on the possible upper bound of the autocorrelation: they report that the peer influence effect on health behaviors holds for up to three degrees of separation (i.e., friends of friends of friends). Their research provides both the upper bound of the extent to which an individual can be influenced by his or her social network, and a useful cut-off point for autocorrelation in peer behavior. 1.3. Present study This study examines peer influence and selection effects on adolescent smoking initiation by using social distance to classify stability and change in friendship ties over a one-year period. We focus on smoking peers who maintain ties with the focal adolescent over time to assess peer influence effects. Peers who stay one degree away from the focal adolescent are considered to potentially have a “direct influence,” whereas peers who stay two or three degrees away are considered to potentially have an “indirect influence.” Evidence for peer influence on smoking would come from finding that smoking initiation is more likely when the focal adolescent has a greater number of consistent smoking peers who are one degree away (direct influence) or two to three degrees away (indirect
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
influence). We focus on changes in friendship ties over time – selectively forming/dropping friendships based on the peers’ smoking status (selection/de-selection) – to assess peer selection effects. Evidence for peer selection on smoking would come from finding that smoking initiation is more likely when the focal adolescent has more new friends and/or fewer ex-friends who are smokers. Note that we consider only changes in friendship status from being more than three degrees away from the focal adolescent to being a friend of the focal adolescent (or vice versa) as evidence of a “pure” (de)selection effect. The forming or dropping of friendships with peers who are two or three degrees away may reflect a “clique effect” (the effects that network members who are not one’s immediate friends exercise on the focal adolescent). Influence and selection effects occur within a larger social context, such as the school setting, that may accept or sanction smoking (Flay et al., 1998; Chen et al., 2006). We posit that the environmental influence for smoking susceptibility (Leatherdale et al., 2005a,b) in the school setting acts as a potential moderator of adolescent smoking initiation. 2. Methods 2.1. Sample Data come from the National Longitudinal Study of Adolescent Health, a study of adolescents from grade 7 through grade 12 at baseline (Bearman et al., 1997). Using a stratified sampling design, 80 high schools were randomly selected from schools across the U.S. based on a combination of factors such as region, urbanicity, racial composition, and size. Of the schools sampled, 16 were selected for the so-called “saturated” sample: the entire school’s student body was interviewed, creating a map of the complete socio-centric school network. We initially restricted the sample to individuals in the saturated school sample who were observed in both Wave I and Wave II (one year later) and who had not smoked at Wave I, resulting in a sample size of N = 2065 (Table 1), of which 250 were smoking initiators by Wave II. This restriction does not apply to the estimation of smoking status of the peers. We did not explicitly model the time dimension, but instead constructed indicators of changes in behavior and friendship ties to reflect the time component. 2.2. Measures 2.2.1. Smoking behavior. Focal adolescents and their peers were classified at each Wave as a “smoker” if they reported smoking at least four days in the past 30 days. We set the cut-off point at four days because this is the median number of days smoked in the past 30 days for smokers in the entire Add Health dataset. In addition, we did not want to consider adolescents who had tried smoking only once or twice as “smokers.” For the analyses that examined changes in smoking behavior between Waves I and II, “initiators” were those who did not smoke at all at Wave I and at least four days at Wave II, “consistent smokers” were those classified as a smoker at both Waves, and “consistent non-smokers” were those classified as a non-smoker (i.e., zero days) at both Waves. Consistent smokers are only used for estimating the peers’ smoking status. 2.2.2. Friendship ties. We calculated the degrees of separation between any two subjects in the complete socio-centric school network, then estimated the numbers of smoking/non-smoking peers for each focal adolescent at all degrees of separation. Nine indicators of friendship ties were derived to capture direct influence effects (1 variable), indirect influence effects (2 variables), selection effects (3 variables), and de-selection effects (3 variables).
349
Direct influence is measured as the number of smokers who maintained friendship with the focal adolescent in both waves, indirect influence 2-degrees as the number of smoking peers who were the friends of the friends of the focal adolescent (i.e., two degrees of separation) in both waves, and indirect influence 3-degrees as the number of smoking peers who stayed three degrees away from the focal adolescent in both waves. The selection effect variables are derived as the number of smoking peers who were two degrees away (selection 2-degrees), three degrees away (selection 3-degrees), or more than three degrees away from or unconnected to (selection >3-degrees) the focal adolescent at Wave I, but became friends with the focal adolescent by Wave II. The de-selection effect variables are derived as the number of smoking peers who were friends with the focal adolescent at Wave I, but became two degrees away (de-selection 2-degrees), three degrees away (deselection 3-degrees), or more than three degrees away from or unconnected to (de-selection >3-degrees) the focal adolescent by Wave II. 2.2.3. School smoking norm. We modeled the social environment factor (i.e., the prevailing school social norm towards smoking) as the proportion of consistent non-smokers in the rest of the school social network outside each individual 3-degree social space. The proportion of non-smokers at the school is a convenient proxy for environmental factors such as cigarette availability and school policy towards smoking, and defining the zone of social norm to be more than 3 degrees of separation away from the focal adolescent allowed for the separate identification of the school social norm effect from the peer effects within the sphere of influence (Leatherdale et al., 2005a,b). 2.2.4. Covariates. Analyses control for the following Wave I variables: gender, grade in school, race/ethnicity, parental education, grade point average, whether the resident parent(s) smoked, depression (5 items), self-esteem (3 items), school attachment (5 items), and delinquency (11 items). All scales had acceptable internal consistency (˛ > 0.77) and information on these measures is presented elsewhere (Perreira et al., 2005; Dornbusch et al., 2001). 2.3. Analytic approach 2.3.1. Missing data analysis. Thirty percent of the adolescents in the analytic sample had at least one missing observation. Prior to analyzing the data we first imputed the missing data in the analysis using the expectation maximization (EM) with importance sampling (King et al., 2001). This algorithm created five sets of imputed datasets, and we ran identical analysis on each dataset. Then the resulting five sets of estimates were combined into a single set of inferences (Schafer, 1997). This operation has the effect of increasing the standard errors of the estimates in order to reflect the uncertainty of imputed missing values. The software package Amelia (Honaker et al., 2008), implemented in the R programming environment, was used for the imputation of missing data. 2.3.2. Mixed effects model and propensity score weights. The data analysis then proceeded in two steps. We first ran a mixed effects logit model in three different variable combinations: influence variables only, influence and selection variables together, and a full model that interacted the influence and selection variables with the indicator of school smoking norm. Given the natural clustering of observations by school, we chose the mixed effects model for the analysis. In addition, in order to adjust for pre-existing differences between initiators and consistent non-smokers in the study population (Rubin and Thomas, 1996), we ran a separate logit regression model with propensity score weights.
350
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
The rationale for adding the propensity scores analysis to an already comprehensive specification for the mixed effects model is twofold: first, even with a large set of controls, there remains the possibility that the distribution of background variables for smoking initiators and consistent non-smokers might be significantly different. Without correcting for this bias, one may conclude that smoking initiation is due to peer effects when they are actually associated with some of the observable characteristics that the subject and his/her peers have in common. Second, while the mixed effects model fully implements the conceptualization of social distance as outlined in Section 1, its very complexity may lead to the overfitting and/or multicollinearity problems, which in turn may affect the generalizability and the accuracy of the estimates. But the downside of the propensity score model is that it reduces detailed peer effects that are shown in the mixed effects model into binary indicators. We therefore utilized both of these complementary approaches to obtain a more complete picture of peer effects on adolescent smoking that is robust to selection bias and overfitting. For the first part of the analysis, we used the lme4 package implementation in R (Bates et al., 2008) to fit the generalized mixed effects model. The specification for the full model is as follows: −1
Pr(yi = 1) = logit j ∼N(0 , 2 )
P(Y = 1) P(Y = 0)
2.3.3. Overfitting and multicollinearity. In order to address the issue of overfitting problem, we validated our results with the receiver-operating characteristics (ROC) with cross-validation (Bradley, 1997; Fawcett, 2006). We used cross-validation with 1000 iterations to determine the mean AUC and the associated 95% confidence interval for each model specification (area-under-curve (AUC) of less than 0.7 is considered to be indicative of overfitting problem.) We also tested for multi-collinearity by estimating the variance-inflation-factor (VIF) of the coefficients and found that these are less than the critical threshold of 5.
(j + sj[−i] + Xi,selection + Xi,influence +sj[−i] ×Xi,selection + sj[−i] ×Xi,influence + Xi,covariates )
where i = 1, . . . , N individuals and j = 1, . . . , J schools in the sample.
(1)
yi is the smoking initiation (1 = Yes; 0 = No) indicator, qj is the varying school intercept, sj[−i] is the proportion of consistent nonsmokers in the school (excluding those who lay in the individual i’s social space), and Xselection and Xinfluence are the sets of influence and selection variables of interest, respectively. sj[−i] × Xselection and sj[−i] × Xinfluence are the respective sets of interactions with the school social norm variable. Finally, Xcovariates is the set of demographic and personal characteristic variables. The aim of the propensity scores is to make balanced comparison between the treatment and comparison groups as in a randomized controlled trial, albeit with observational data (Rosenbaum and Rubin, 1983). Here “treatment” denotes the presence of characteristics or behaviors of interest, which in this study is smoking initiation. Balancing involves creating weights that quantifies the comparison group’s similarity to the “treatment” group. These probability weights, also called propensity score weights, are estimated with the boosting technique (McCaffrey et al., 2004). In the present context propensity scores attribute more importance to the cases that do not report having smoking peers yet are similar to smokers in their background characteristics (Ridgeway, 2006; Morral et al., 2004). log
new smoking friends 2–3 degrees away > 0; 0 otherwise) was chosen as the “treatment” group, since the cases that correspond to this particular combination of effects are the ones that are most interesting in terms of policy. We obtained the propensity score weights in the form of odds by running generalized boosted regression of treatment assignment on the set of covariates, using the gbm package in R (Ridgeway, 2007). Since the use of propensity weights can bias the error estimates, sandwich estimators (Zeileis, 2004) for the variance in the weighted propensity score model were used to obtain robust standard errors.
= ˇ0 + T1 + T2 + T3 +
K
ˇk Xk
(2)
k=1
Because there are three vectors of “treatment” (number of former, same, and new friends for each degree of separation) for selection, de-selection, and influence effects, it was not feasible to apply the usual application of propensity score modeling, which only allows binary categorization. We instead built a multiindicator propensity score model with three indicators, in which T1 , T2 , T3 are binary indicators that stand for selection, de-selection, and influence effects respectively (three social distance vectors were summarized into one single binary indicator for each of the effects). The combination of positive selection, no de-selection, and positive influence (T1 = 1 if the number of new smoking friends initially >3 degrees away > 0; 0 otherwise; T2 = 0 (1 if the number of former smoking friends 2 or more degrees away > 0; 0 otherwise); T3 = 1 if the number of consistent smoking friends OR the number of
3. Results 3.1. Peer effects on smoking initiation Results from the mixed effects regression shown in Table 2 indicate a significant direct influence effect on smoking initiation (Model 1): each consistent smoking friend who is 1 degree away increases the likelihood of an adolescent starting to smoke by 80% (OR = 1.79, 95% CI = 1.38, 2.34). After adding the selection and de-selection variables (Model 2), this direct influence effect remains essentially unchanged. Model 2 also shows evidence of both a selection and de-selection effect on smoking initiation. Selecting a new smoking friend who was initially more than three degrees away from the focal adolescent (OR = 2.08, 95% CI = 1.86, 2.93), befriending a friend’s smoking friend (two degrees away; OR = 1.63, 95% CI = 1.03, 2.59), and de-selecting a smoking friend (more than three degrees away; OR = 1.29, 95% CI = 1.02, 1.64) are all associated with an increased likelihood of smoke uptake. When the school norm × peer effect interaction terms are added to the model (Model 3), the two-degree selection effect and the de-selection effect are no longer significant, implying that their positive results were actually due to the significant presence of smokers at the school. The selection and direct influence effects remain significant, however. The addition of new smoking peers from three degrees away more than doubles the likelihood that the adolescent starts smoking (OR = 2.18, 95% CI = 1.27, 3.76), and having a consistent smoking friend also more than doubles the odds of smoking uptake (OR = 2.40, 95% CI = 1.14, 5.06). While the addition of interaction terms decreases the direct influence effect (OR increases from 1.79 to 2.40), it does not reduce the selection effect as much (OR increases from 2.08 to 2.18). This seems to indicate that the direct influence effect is more affected by the level of school social norm towards smoking (the corresponding interaction term is negative in logit coefficient but not significant) than is the selection effect. 3.2. Other predictors of smoking initiation Across the three models shown in Table 2, the same covariates emerge as significant predictors of smoking initiation.
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
351
Table 2 Multilevel models predicting smoking initiation from indicators of peer selection, peer influence, school social norm, and covariates. Variable
OR School social norm % consistent non-smokers at the school 0.66 Indicator of peer selection # new smoking friends initially >3 degrees away # new smoking friends initially 3 degrees away # new smoking friends initially 2 degrees away Peer de-selection # former smoking friends now >3 degrees away # former smoking friends now 3 degrees away # former smoking friends now 2 degrees away Indicator of direct peer influence 1.80 # of consistent smoking friends 1 degree away Indicators of indirect peer influence 0.94 # consistent smoking peers 2 degrees away 1.03 # consistent smoking friends 3 degrees away School norm × peer effect interactions School norm × selection (>3 degrees) School norm × de-selection (>3 degrees) School norm × selection (3 degrees) School norm × de-selection (3 degrees) School norm × selection (2 degrees) School norm × de-selection (2 degrees) School norm × direct influence (1 degree) School norm × indirect influence (2 degrees) School norm × indirect influence (3 degrees) Background characteristics 0.97 Gender: female Grade in school (reference group: grade 7) 1.70 Grade 8 1.62 Grade 9 1.19 Grade 10 1.34 Grade 11 0.87 Grade 12 Race/ethnicity (reference group: White) 0.45 African American Asian 0.88 1.04 Hispanic Parent education (reference group: less than HS) 1.23 High school graduate/GED 1.11 Some college 0.76 College degree or higher 1.06 Depression Grade point average 0.68 Self esteem 1.00 0.86 School attachment Delinquency 3.41 Mother smokes 1.06 1.78 Father smokes ROC 0.69 Area under curve (AUC) * ** ***
Influence + selection (Model 2)
Influence (Model 1) 95% CI
Sig.
(0.20, 2.17)
OR
95% CI
0.86
(0.29, 2.54)
Full model (Model 3) Sig.
***
OR
95% CI
0.93
(0.27, 3.23)
2.18
(1.27, 3.76)
5.55
(0.68, 45.33)
2.08
(1.68, 2.58)
1.56
(0.87, 2.79)
1.63
(1.03, 2.59)
*
1.60
(0.54, 4.68)
1.29
(1.02, 1.64)
*
1.04
(0.51, 2.14)
1.16
(0.55, 2.42)
0.91
(0.10, 7.90)
1.06
(0.63, 1.77)
0.36
(0.07, 1.97)
1.79
(1.38, 2.34)
2.40
(1.14, 5.06)
(0.73, 1.21)
0.84
(0.64, 1.11)
0.59
(0.25, 1.38)
(0.91, 1.16)
1.02
(0.89, 1.17)
1.35
(0.65, 2.82)
(1.39, 2.33)
***
***
0.89 1.99 0.02 2.24 1.14 33.1 0.47 3.09 0.37
(0.03, 4.83)
0.95
(0.70, 1.29)
0.94
(0.69, 1.28)
(0.85, 3.38) (0.76, 3.46) (0.60, 2.35) (0.67, 2.71) (0.34, 2.23)
1.57 1.15 1.09 1.17 0.81
(0.79, 3.13) (0.53, 2.49) (0.57, 2.10) (0.60, 2.28) (0.32, 2.05)
1.54 1.12 1.06 1.12 0.78
(0.77, 3.07) (0.51, 2.44) (0.55, 2.06) (0.57, 2.21) (0.31, 1.98)
0.49 0.81 1.03
(0.27, 0.89) (0.48, 1.38) (0.60, 1.76)
0.49 0.79 1.02
(0.27, 0.89) (0.46, 1.36) (0.60, 1.75)
1.26 1.17 0.83 1.06 0.71 1.01 0.81 3.04 0.99 1.89
(0.75, 2.12) (0.66, 2.09) (0.46, 1.48) (1.00, 1.11) (0.57, 0.89) (0.78, 1.31) (0.66, 1.00) (2.03, 4.56) (0.73, 1.36) (1.39, 2.57)
1.26 1.16 0.82 1.06 0.71 1.01 0.81 2.96 0.98 1.92
(0.75, 2.12) (0.65, 2.06) (0.46, 1.47) (1.01, 1.11) (0.57, 0.90) (0.78, 1.31) (0.65, 1.00) (1.97, 4.44) (0.72, 1.35) (1.41, 2.62)
0.71
(0.67, 0.74)
0.71
(0.66, 0.74)
(0.74, 2.04) (0.63, 1.95) (0.43, 1.36) (1.01, 1.11) (0.54, 0.84) (0.78, 1.29) (0.69, 1.05) (2.30, 5.05) (0.78, 1.44) (1.32, 2.41)
*
* ***
* ***
***
(0.66, 0.71)
*
* **
* ***
***
***
*
(0.26, 3.03) (0.23, 17.08) (0.00, 12.65) (0.00, 1629) (0.07, 19.30) (0.19, 5750) (0.07, 3.10) (0.26, 37.3)
(0.71, 1.31)
(0.25, 0.83) (0.49, 1.58) (0.58, 1.86)
Sig.
*
* **
* ***
***
p < 0.05. p < 0.01. p < 0.00.
Initiation is less likely among adolescents who are African American (compared to non-Hispanic white), have a higher grade point average, and have a stronger school attachment. Adolescents are more likely to start smoking if they have more depressive symptoms, greater involvement in delinquency, and a father who smokes.
3.3. Results from propensity score model Fig. 2 shows the graph of Kolmogorov–Smirnov statistics (showing that the difference in the underlying distribution between the treatment and comparison groups is minimized (towards the center-line) when the comparison group is weighted with the
352
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Fig. 2. Propensity score balance check: Kolmogorov–Smirnov statistics.
propensity scores (“+”)), indicating that the unweighted averages of the covariates differ substantially between the comparison and treatment groups, but with the application of propensity score weights these differences diminish substantially for most variables. This demonstrates that the propensity score adjustment reduces pre-existing background differences between groups. Results of the propensity score model are consistent with the pattern of results from the mixed effect regression models (Table 3). In terms of peer effects, the selection effect adjusted with the propensity score weights is significantly associated with smoking initiation (OR = 3.04, 95% CI = 2.18, 4.22), as well as the direct influence effect (OR = 1.91, 95% CI = 1.33, 2.75). The addition of interaction terms in Model 5 shows that the effect of selection is virtually the same as in Model 4 (OR = 3.04, 95% CI = 1.22, 7.58), but the influence effect increases the odds by 80% from Model 4 (OR = 3.46, 95% CI = 1.17, 10.28). This change is accompanied by stronger (negative) interaction effect (OR = 0.24, 95% CI = 0.02, 3.19), however. In contrast the corresponding coefficient for the interaction effect of selection is unity. The propensity score model confirms findings from the mixed effects model, suggesting that influence effects are more sensitive than selection effects to school social norms. As for overfitting, the propensity score weights model with its simplified binary specification has an AUC of 0.74 (95% CI = 0.70, 0.78), which is an improvement over the regression models (AUC = 0.69–0.71). Both approaches are therefore internally consistent and should
be generalizable to other adolescent populations in similar school settings. 4. Discussion Adolescents surround themselves with peers of similar backgrounds characteristics (de Leeuw et al., 2008; Kandel, 1978), and some of these characteristics are possibly susceptible to the contextual factors that create a favorable environment for smoking. In this study we applied the concept of social distance in the context of differentiating selection and influence effects on the problem of adolescent smoking initiation. Our study finds compelling evidence for a selection effect on smoking and a direct influence effect of the immediate peers (Engels et al., 1997; Krauth, 2005; Wang et al., 2000), suggesting that adolescent smoking initiation is closely intertwined with personal dynamics, while discounting the role of group dynamics such as cliques in influencing adolescents’ smoking behavior. While results suggest that selection and direct influence effects are both important drivers of smoking initiation, their modus operandi is different. The strength of direct peer influence seems related to the prevailing school social norms towards smoking, with the impact of direct peer influence being stronger at schools with a higher prevalence of smoking. In contrast, the strength of peer selection (i.e., selecting smoking friends more than three degrees
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
353
Table 3 Propensity score weighted logistic regression (Models 4 and 5). Variable
School social norm % consistent non-smokers at the school Indicators of: Selection De-selection Influence School norm × peer effect interactions School norm × selection School norm × de-selection School norm × influence Background characteristics Gender: female Grade in school (reference group: grade 7) Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 Race/ethnicity (reference group: White) African American Asian Hispanic Parent education (reference group: less than HS) High school graduate/GED Some college College degree or higher Depression Grade point average Self esteem School attachment Delinquency Mother smokes Father smokes ROC Area under curve (AUC) * ** ***
Full model (Model 5)
Influence + selection (Model 4) OR
95% CI
0.89
(0.30, 2.69)
3.04 1.39 1.91
(2.18, 4.22) (0.91, 2.11) (1.33, 2.75)
Sig.
***
***
OR
95% CI
0.82
(0.21, 3.25)
3.04 1.04 3.46
(1.22, 7.58) (0.51, 2.14) (1.17, 10.28)
1.03 15.08 0.24 (0.68, 1.29)
0.92
(0.67, 1.27)
1.60 1.31 1.20 1.27 0.88
(0.76, 3.36) (0.61, 2.83) (0.59, 2.44) (0.62, 2.58) (0.34, 2.31)
1.64 1.39 1.22 1.28 0.90
(0.77, 3.49) (0.64, 3.05) (0.59, 2.51) (0.62, 2.64) (0.34, 2.41)
0.49 0.75 1.00
(0.27, 0.87) (0.43, 1.28) (0.58, 1.73)
0.46 0.69 0.97
(0.25, 0.83) (0.40, 1.20) (0.56, 1.67)
1.42 1.21 0.89 1.05 0.67 1.00 0.83 3.02 1.06 1.79
(0.85, 2.38) (0.69, 2.12) (0.50, 1.58) (1.00, 1.11) (0.50, 0.90) (0.78, 1.30) (0.68, 1.00) (2.00, 4.55) (0.77, 1.46) (1.31, 2.45)
1.47 1.24 0.91 1.06 0.67 1.00 0.83 2.96 1.08 1.82
(0.88, 2.46) (0.7, 2.18) (0.51, 1.62) (1.00, 1.12) (0.50, 0.90) (0.77, 1.29) (0.68, 1.01) (1.95, 4.48) (0.78, 1.49) (1.33, 2.49)
0.74
(0.71, 0.77)
0.74
(0.70, 0.78)
* **
* ***
***
*
*
(0.14, 7.71) (0.89, 255.95) (0.02, 3.19)
0.94
*
Sig.
**
* **
***
***
p < 0.05. p < 0.01. p < 0.00.
away) seems indifferent to school social norms towards smoking, suggesting that this effect may primarily be driven by the adolescent smoker’s need for homophily. In addition, the fact that indirect peer effects are significant only when they are not interacted with the school social norm shows that indirect effects are actually indistinguishable from the effects of larger contextual factors. This contrasts with the 3-degree “induction” (i.e., influence) effect found by Christakis and Fowler (2007, 2008) using Framingham Heart Study data, which suggested that the influence of peers may be found for up to three degrees of separation (i.e., “friends of friends of friends”). In contrast to the direct influence effect, interpretation of the significant selection effect should be more nuanced as we cannot determine whether smoking initiators select smoking peers first and then start smoking, or take up smoking first and select smoking peers based on shared preferences (our definition of selection therefore includes both). Further, factors other than smoking that attract adolescents to their smoking peers (e.g., risky behaviors, physical attraction) are potential confounders of the selection effect (Abel et al., 2002; Ennett and Bauman, 1993; Guo, 2006; Scarr and McCartney, 1983). We addressed the issue of potential confounders with the propensity score weights, and tested for overfitting and multi-collinearity with ROC and VIF, respectively, to address potential concerns on the complexity in model specifications. Results from this study suggest that adolescent smoking homophily is due to the combined effect both of being exposed to smoking peers and actively exercising friendship choice. Our
study shows that smoking initiation among adolescents occurs as part of a close personal interaction, either between existing friends or in the process befriending new friends outside of one’s social space. In addition, we find that “friends of friends” effects are indistinguishable from the broader environmental factors that influence adolescents. Adolescent drug prevention programs typically place a strong emphasis on peer pressure and how to resist it. Our results suggest that it is also important to recognize that: (1) adolescents actively choose friends as well as (risky) behaviors consistent with their substance use and friendship preferences; and (2) the smoking risk to which adolescents are exposed can be ameliorated by targeting the social acceptance of smoking at large. While the effectiveness of prevention programs may be limited by the social dimension of smoking that closely intertwines smoking with friendship, they may be enhanced by targeting the social acceptance of smoking among adolescents.
Role of funding source Funding for this study was provided by grant 16RT-0169 from the California Tobacco-Related Disease Research Program (TRDRP) of the University of California, awarded to Dr. Tucker as Principle Investigator. TRDRP had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
354
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Contributors All authors have been personally and actively involved in substantive work leading to the report, and will hold themselves jointly and individually responsible for its content. Conflict of interest None declared. Data source This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with Cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123W. Franklin Street, Chapel Hill, NC 27516-2524, United States ([email protected]). Connection with tobacco, alcohol, pharmaceutical or gaming industry: None. References Abel, G., Plumridge, L., Graham, P., 2002. Peers, networks or relationships: strategies for understanding social dynamics as determinants of smoking behaviour. DrugEdu. Prev. Policy 9, 325–338. Alexander, C., Piazza, M., Mekos, D., Valente, T., 2001. Peers, schools, and adolescent cigarette smoking. J. Adolesc. Health 29, 22–30. Bates, D., Maechler, M., Dai, B., 2008. lme4: Linear Mixed-effects Models Using S4 Classes., http://lme4.r-forge.r-project.org/. Bearman, P.S., Jones, J., Udry, J.R., 1997. The national longitudinal study of adolescent health: research design http://www.cpc.unc.edu/projects/addhealth/design (retrieved February 2003). Bradley, A., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30, 1145–1159. Calvó-Armengol, A., Patacchini, E., Zenou, Y., 2005. Peer effects and social networks in education and crime. C.E.P.R. Discussion Papers http://ideas.repec.org/p/cpr/ceprdp/5244.html (accessed January 2010). Chen, X., Stanton, B., Fang, X., Li, X., Lin, D., Zhang, J., Liu, H., Yang, H., 2006. Perceived smoking norms, socioenvironmental factors, personal attitudes and adolescent smoking in China: a mediation analysis with longitudinal data. J. Adolesc. Health 38, 359–368. Christakis, N.A., Fowler, J.H., 2007. The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357, 370–379. Christakis, N.A., Fowler, J.H., 2008. The collective dynamics of smoking in a large social network. N. Engl. J. Med. 358, 2249–2258. de Leeuw, R., Scholte, R., Harakeh, Z., van Leeuwe, J., Engels, R., 2008. Parental smoking-specific communication, adolescents smoking behavior and friendship selection. J. Youth Adolesc. 37, 1229–1241. Dishion, T.J., McCord, J., Poulin, F., 1999. When interventions harm: peer groups and problem behavior. Am. Psychol. 54, 755–764. Dornbusch, S., Erickson, K., Laird, J., 2001. The relation of family and school attachment to adolescent deviance in diverse groups and communities. J. Adolesc. Res. 16, 396–422. Engels, R., Knibbe, R.A., Drop, M.J., deHaan, Y.T., 1997. Homogeneity of cigarette smoking within peer groups: influence or selection? Health Educ. Behav. 24, 801–811. Ennett, S.T., Bauman, K.E., 1993. Peer group structure and adolescent cigarette smoking: a social network analysis. J. Health Soc. Behav. 34, 226–236.
Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874. Flay, B.R., Hu, F.B., Richardson, J., 1998. Psychosocial predictors of different stages of cigarette smoking among high school students. Prev. Med. 27, A9–A18. Go, M.-H., Green Jr., H.D., Kennedy, D.P., Pollard, M., Tucker, J.S., 2010. Peer influence and selection effects on adolescent smoking. Drug Alcohol Depend. 109, 239–242. Guo, G., 2006. Genetic similarity shared by best friends among adolescents. Twin Res. Hum. Genet. 9, 113–121. Hall, J.A., Valente, T.W., 2007. Adolescent smoking networks: the effects of influence and selection on future smoking. Addict. Behav. 32, 3054–3059. Honaker, J., King, G., Blackwell, M., 2008. Amelia: Amelia II: A Program for Missing Data., http://gking.harvard.edu/amelia (accessed January 2010). Kandel, D.B., 1978. Homophily, selection, and socialization in adolescent friendships. Am. J. Sociol. 84, 427. King, G., Honaker, J., Joseph, A., Scheve, K., 2001. Analyzing incomplete political science data: an alternative algorithm for multiple imputation. Am. Polit. Sci. Rev. 95, 49–69. Kirke, D.M., 2004. Chain reactions in adolescents’ cigarette, alcohol and drug use: similarity through peer influence or the patterning of ties in peer networks? Soc. Networks 26, 3–28. Kobus, K., 2003. Peers and adolescent smoking. Addiction 98, 37–55. Krauth, B.V., 2005. Peer effects and selection effects on smoking among Canadian youth. Can. J. Econ. 38, 735–757. Leatherdale, S.T., Brown, K.S., McDonald, P.W., Cameron, R., 2005a. Social modeling in the school environment, student characteristics, and smoking susceptibility: a multi-level analysis. J. Adolesc. Health 37, 330–336. Leatherdale, S.T., McDonald, P.W., Cameron, R., Brown, K.S., 2005b. A multilevel analysis examining the relationship between social influences for smoking and smoking onset. Am. J. Health Behav. 29, 520–530. Mariano, K.A., Harton, H.C., 2005. Similarities in aggression, inattention/hyperactivity, depression, and anxiety in middle childhood friendships. J. Soc. Clin. Psychol. 24, 471–496. McCaffrey, D.F., Ridgeway, G., Morral, A.R., 2004. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol. Methods 9, 403–425. Mercken, L., Snijders, T., Candel, M., Steglich, C., De Vries, H., 2005. Why adolescents start to smoke: peer influence versus peer selection processes. Psychol. Health 20 (Supp. 1), 170. Morral, A.R., McCaffrey, D.F., Ridgeway, G., 2004. Effectiveness of communitybased treatment for substance-abusing adolescents: 12-month outcomes of youths entering Phoenix Academy or alternative probation dispositions. Psychol. Addict. Behav. 18, 257–268. Perreira, K., Deeb-Sossa, N., Harris, K., 2005. What are we measuring-an evaluation of the CES-D across race/ethnicity and immigrant generation. Soc. Forces 83, 1567–1602. Ridgeway, G., 2006. Assessing the effect of race bias in post-traffic stop outcomes using propensity scores. J. Quant. Criminol. 22, 1–29. Ridgeway, G., 2007. gbm: Generalized Boosted Regression Models., http://www.ipensieri.com/gregr/gbm.shtml (accessed January 2010). Rosenbaum, P.R., Rubin, D.B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55. Rosenquist, J.N., Murabito, J., Fowler, J.H., Christakis, N.A., 2010. The spread of alcohol consumption behavior in a large social network. Ann. Intern. Med. 152, 426–433. Rubin, D.B., Thomas, N., 1996. Matching using estimated propensity scores: relating theory to practice. Biometrics 52, 249–264. Scarr, S., McCartney, K., 1983. How people make their own environments: a theory of genotype, environment effects. Child Dev. 54, 424–435. Schafer, J., 1997. Analysis of Incomplete Multivariate Data. Chapman and Hall/CRC, London. Snijders, T., Pearson, M., 2010. Dynamic networks and behavior: separating selection from influence. Soc. Methodol. 40, 329–393. Snijders, T., 2001. The statistical evaluation of social network dynamics. Soc. Methodol. 31, 361–395. Urberg, K., De˘girmencio˘glu, S.M., Pilgram, C., 1997. Close friend and group influence on adolescent cigarette smoking and alcohol use. Dev. Psychol. 33, 834–844. Wang, M.Q., Eddy, J.M., Fitzhugh, E.C., 2000. Smoking acquisition: peer influence and self-selection. Psychol. Rep. 86, 1241–1246. Zeileis, A., 2004. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 10, 1–17.
Contents lists available at SciVerse ScienceDirect
Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep
Social distance and homophily in adolescent smoking initiation Myong-Hyun Go a,∗ , Joan S. Tucker b , Harold D. Green Jr. b , Michael Pollard b , David Kennedy b a b
UCLA Center for Biomedical Modeling, United States RAND Corporation, Santa Monica, CA, United States
a r t i c l e
i n f o
Article history: Received 30 September 2011 Received in revised form 2 February 2012 Accepted 7 February 2012 Available online 12 March 2012 Keywords: Adolescent Smoking Influence Selection Longitudinal Social network Propensity score
a b s t r a c t Background: Studies often demonstrate homophily in adolescent smoking behavior, but rarely investigate the extent to which this is due to the peer network processes of selection versus influence. Applying the concept of social distance, this study examines these two processes for smoking initiation. Methods: We analyzed socio-centric network data from the National Longitudinal Study of Adolescent Health (N = 2065; grades = 7th–12th). Social distance (degrees of separation), combined with stability and change in friendship networks, was used to derive indicators of peer selection and influence on initiation. Multilevel modeling was used to predict initiation from these indicators, and propensity score modeling was used to determine whether these associations remained after adjusting for pre-existing differences between initiators and consistent non-smokers. Results: We found that both peer influence and selection effects increased the likelihood of initiation even after adjusting with propensity score weights and demographic controls. While the effect size for peer influence depended on the overall proportion of smokers at the school, the selection effect was independent of the school environment. De-selection and indirect influence effects were not significant after controlling for school norm interactions. Conclusions: The association between peer smoking and adolescent smoking initiation appears to be due to both peer selection and direct influence. However, “friends of friends” effects are likely to be confounded with contextual factors. Given that smoking initiation is primarily associated with close personal interactions between the adolescent and his/her friends, prevention efforts should focus on the role of smoking in fostering personal relationships among adolescents. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction 1.1. Homophily in adolescent smoking behavior Peer smoking is one of the strongest and most consistent predictors of adolescent smoking (Kobus, 2003). However, the nature of this association is not well understood. Studies reporting a correlation between the smoking behavior of adolescents and their peers typically infer that this association is the result of a peer influence effect, ignoring the possibility that adolescents may choose friends who are similar to themselves in terms of smoking. Influence involves an individual’s attributes changing in response to the predominant behavior in his/her social network. Selection involves structural changes in an individual’s social network as the result of the individual selecting to form or dissolve friendships with peers who hold certain attributes; for example, an adolescent may choose
∗ Corresponding author at: UCLA Center for Biomedical Modeling, 10940 Wilshire Blvd Suite 1450, Los Angeles, CA 90024, United States. E-mail address: [email protected] (M.-H. Go). 0376-8716/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2012.02.007
to form friendships with other smokers, or sever ties with peers who smoke. Both influence and selection effects are processes that lead to the homogeneity of peer networks. Adolescents tend to associate with peers who have similar attributes as their own such as age and socioeconomic status (Mariano and Harton, 2005). In addition, adolescents exercise peer pressure on others, which accelerates the process of assimilation (Dishion et al., 1999). Since peer influence and selection effects are both processes of network homogenization rather than its outcomes, the two are indistinguishable if one ignores pathways of network evolution and levels of network homogeneity over time (Alexander et al., 2001; Hall and Valente, 2007; Kirke, 2004). As a result, any identification strategy has to involve at the minimum some form of beforeand-after longitudinal approach to differentiate the two effects (Go et al., 2010; Urberg et al., 1997; Snijders and Pearson, 2010). The longitudinal framework by itself, however, is not sufficient for resolving the question of whether peer selection precedes smoking initiation or the other way around. That is, smoking initiation can bring new friends, but new friendships can also result in smoking initiation. Without “high resolution” data that records minute moments of behavioral changes, one cannot infer from the data the
348
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Table 1 Descriptive statistics for main study variables. Variables
Minimum
Peer network variables Indicators of peer selection 0 # new smoking friends initially >3 degrees away 0 # new smoking friends initially 3 degrees away 0 # new smoking friends initially 2 degrees away Indicators of peer de-selection # former smoking 0 friends now >3 degrees away # former smoking 0 friends now 3 degrees away 0 # former smoking friends now 2 degrees away Indicators of peer influence 0 # consistent smoking friends 1 degree away (direct influence) 0 # consistent smoking peers 2 degrees away (indirect influence) 0 # consistent smoking peers 3 degrees away (indirect influence) 1 # nominated peers at Wave I % consistent 22% non-smokers at the school at Wave I Personal characteristics at Wave I 0 Depression (higher = worse) 1 Grade point average (higher = better) 0 Self-esteem (higher = better) 0 School attachment (higher = better) 0 Delinquency (higher = worse)
Median
Mean
Maximum
0
0.20
6
0
0.05
3
0
0.06
2
0
0.24
6
0
0.03
3
0
0.04
5
0
0.17
6
0
0.20
6
0
0.35
12
2
2.39
10
50%
56%
100%
3
3.41
15
3
2.89
4
4
4.07
5
3
2.85
4
0.13
0.25
3
Variables
%
Female Grade in school at Wave I 7th 8th 9th 10th 11th 12th Race/ethnicity White African American Hispanic Asian Parent education at Wave I Did not finish high school High school graduate/GED Some college College degree or higher Parental smoking at Wave I Mother smokes Father smokes
50.5 11.0 11.3 10.9 31.5 28.7 6.6 57.4 17.9 14.2 10.5 12.0 32.3 22.3 33.4 36.5 42.3
temporal order of smoking initiation vis-à-vis peer selection. However, we propose an innovative approach based on the concept of “social distance” (i.e., degrees of separation) that controls for the autocorrelation of behaviors in the friendship networks (Mercken et al., 2005) and provides a more stringent test of peer influence and selection effects.
Fig. 1. Social space (degrees of separation).
1.2. Differentiating peer influence from selection: the role of social distance Selection is best expressed when an adolescent chooses a peer with similar attributes or behaviors as his or her own. If selection on smoking is a significant driver of smoking homophily, statistical analysis will show that an adolescent smoker is more likely to choose smoking peers as friends rather than the non-smoking peers. Similarly for peer influence, one would observe that the smoking behavior of the focal adolescent synchronizes over time with the predominant smoking behavior of the adolescent’s peers. For example, exposure to smoking peers in terms of both quantity (the number of smoking peers) and duration (e.g., one year) should increase the likelihood of smoking uptake compared to being exposed to non-smoking peers. The task of differentiating selection and influence effects further requires controlling for the problem of autocorrelation, which percolates through the entire network and makes it difficult to properly attribute the source of influence to the immediate peers (Calvó-Armengol et al., 2005; Snijders, 2001). Our approach to addressing the problem of identifying peer influence and selection effects starts with the degrees of separation (i.e., “social distance”) in the adolescent’s social space (Fig. 1). The strength of influence on the focal adolescent by a peer should be proportional to the social distance between the two individuals. For example, friends, who are one degree away, should exert more influence on the adolescent’s behavior than “friends of friends” who are two degrees away. In the case of peer selection, the effect would be clearest when the adolescent is willing to bear the “high cost” of initiating friendship with a socially distant smoker. When an adolescent is willing to befriend a distant smoker (in terms of degree of separation), it is likely that the effect is due to selection without being “contaminated” by the influence effect or some other random factor. Studies by Christakis, Fowler and colleagues (Christakis and Fowler, 2007, 2008; Rosenquist et al., 2010) provide information on the possible upper bound of the autocorrelation: they report that the peer influence effect on health behaviors holds for up to three degrees of separation (i.e., friends of friends of friends). Their research provides both the upper bound of the extent to which an individual can be influenced by his or her social network, and a useful cut-off point for autocorrelation in peer behavior. 1.3. Present study This study examines peer influence and selection effects on adolescent smoking initiation by using social distance to classify stability and change in friendship ties over a one-year period. We focus on smoking peers who maintain ties with the focal adolescent over time to assess peer influence effects. Peers who stay one degree away from the focal adolescent are considered to potentially have a “direct influence,” whereas peers who stay two or three degrees away are considered to potentially have an “indirect influence.” Evidence for peer influence on smoking would come from finding that smoking initiation is more likely when the focal adolescent has a greater number of consistent smoking peers who are one degree away (direct influence) or two to three degrees away (indirect
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
influence). We focus on changes in friendship ties over time – selectively forming/dropping friendships based on the peers’ smoking status (selection/de-selection) – to assess peer selection effects. Evidence for peer selection on smoking would come from finding that smoking initiation is more likely when the focal adolescent has more new friends and/or fewer ex-friends who are smokers. Note that we consider only changes in friendship status from being more than three degrees away from the focal adolescent to being a friend of the focal adolescent (or vice versa) as evidence of a “pure” (de)selection effect. The forming or dropping of friendships with peers who are two or three degrees away may reflect a “clique effect” (the effects that network members who are not one’s immediate friends exercise on the focal adolescent). Influence and selection effects occur within a larger social context, such as the school setting, that may accept or sanction smoking (Flay et al., 1998; Chen et al., 2006). We posit that the environmental influence for smoking susceptibility (Leatherdale et al., 2005a,b) in the school setting acts as a potential moderator of adolescent smoking initiation. 2. Methods 2.1. Sample Data come from the National Longitudinal Study of Adolescent Health, a study of adolescents from grade 7 through grade 12 at baseline (Bearman et al., 1997). Using a stratified sampling design, 80 high schools were randomly selected from schools across the U.S. based on a combination of factors such as region, urbanicity, racial composition, and size. Of the schools sampled, 16 were selected for the so-called “saturated” sample: the entire school’s student body was interviewed, creating a map of the complete socio-centric school network. We initially restricted the sample to individuals in the saturated school sample who were observed in both Wave I and Wave II (one year later) and who had not smoked at Wave I, resulting in a sample size of N = 2065 (Table 1), of which 250 were smoking initiators by Wave II. This restriction does not apply to the estimation of smoking status of the peers. We did not explicitly model the time dimension, but instead constructed indicators of changes in behavior and friendship ties to reflect the time component. 2.2. Measures 2.2.1. Smoking behavior. Focal adolescents and their peers were classified at each Wave as a “smoker” if they reported smoking at least four days in the past 30 days. We set the cut-off point at four days because this is the median number of days smoked in the past 30 days for smokers in the entire Add Health dataset. In addition, we did not want to consider adolescents who had tried smoking only once or twice as “smokers.” For the analyses that examined changes in smoking behavior between Waves I and II, “initiators” were those who did not smoke at all at Wave I and at least four days at Wave II, “consistent smokers” were those classified as a smoker at both Waves, and “consistent non-smokers” were those classified as a non-smoker (i.e., zero days) at both Waves. Consistent smokers are only used for estimating the peers’ smoking status. 2.2.2. Friendship ties. We calculated the degrees of separation between any two subjects in the complete socio-centric school network, then estimated the numbers of smoking/non-smoking peers for each focal adolescent at all degrees of separation. Nine indicators of friendship ties were derived to capture direct influence effects (1 variable), indirect influence effects (2 variables), selection effects (3 variables), and de-selection effects (3 variables).
349
Direct influence is measured as the number of smokers who maintained friendship with the focal adolescent in both waves, indirect influence 2-degrees as the number of smoking peers who were the friends of the friends of the focal adolescent (i.e., two degrees of separation) in both waves, and indirect influence 3-degrees as the number of smoking peers who stayed three degrees away from the focal adolescent in both waves. The selection effect variables are derived as the number of smoking peers who were two degrees away (selection 2-degrees), three degrees away (selection 3-degrees), or more than three degrees away from or unconnected to (selection >3-degrees) the focal adolescent at Wave I, but became friends with the focal adolescent by Wave II. The de-selection effect variables are derived as the number of smoking peers who were friends with the focal adolescent at Wave I, but became two degrees away (de-selection 2-degrees), three degrees away (deselection 3-degrees), or more than three degrees away from or unconnected to (de-selection >3-degrees) the focal adolescent by Wave II. 2.2.3. School smoking norm. We modeled the social environment factor (i.e., the prevailing school social norm towards smoking) as the proportion of consistent non-smokers in the rest of the school social network outside each individual 3-degree social space. The proportion of non-smokers at the school is a convenient proxy for environmental factors such as cigarette availability and school policy towards smoking, and defining the zone of social norm to be more than 3 degrees of separation away from the focal adolescent allowed for the separate identification of the school social norm effect from the peer effects within the sphere of influence (Leatherdale et al., 2005a,b). 2.2.4. Covariates. Analyses control for the following Wave I variables: gender, grade in school, race/ethnicity, parental education, grade point average, whether the resident parent(s) smoked, depression (5 items), self-esteem (3 items), school attachment (5 items), and delinquency (11 items). All scales had acceptable internal consistency (˛ > 0.77) and information on these measures is presented elsewhere (Perreira et al., 2005; Dornbusch et al., 2001). 2.3. Analytic approach 2.3.1. Missing data analysis. Thirty percent of the adolescents in the analytic sample had at least one missing observation. Prior to analyzing the data we first imputed the missing data in the analysis using the expectation maximization (EM) with importance sampling (King et al., 2001). This algorithm created five sets of imputed datasets, and we ran identical analysis on each dataset. Then the resulting five sets of estimates were combined into a single set of inferences (Schafer, 1997). This operation has the effect of increasing the standard errors of the estimates in order to reflect the uncertainty of imputed missing values. The software package Amelia (Honaker et al., 2008), implemented in the R programming environment, was used for the imputation of missing data. 2.3.2. Mixed effects model and propensity score weights. The data analysis then proceeded in two steps. We first ran a mixed effects logit model in three different variable combinations: influence variables only, influence and selection variables together, and a full model that interacted the influence and selection variables with the indicator of school smoking norm. Given the natural clustering of observations by school, we chose the mixed effects model for the analysis. In addition, in order to adjust for pre-existing differences between initiators and consistent non-smokers in the study population (Rubin and Thomas, 1996), we ran a separate logit regression model with propensity score weights.
350
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
The rationale for adding the propensity scores analysis to an already comprehensive specification for the mixed effects model is twofold: first, even with a large set of controls, there remains the possibility that the distribution of background variables for smoking initiators and consistent non-smokers might be significantly different. Without correcting for this bias, one may conclude that smoking initiation is due to peer effects when they are actually associated with some of the observable characteristics that the subject and his/her peers have in common. Second, while the mixed effects model fully implements the conceptualization of social distance as outlined in Section 1, its very complexity may lead to the overfitting and/or multicollinearity problems, which in turn may affect the generalizability and the accuracy of the estimates. But the downside of the propensity score model is that it reduces detailed peer effects that are shown in the mixed effects model into binary indicators. We therefore utilized both of these complementary approaches to obtain a more complete picture of peer effects on adolescent smoking that is robust to selection bias and overfitting. For the first part of the analysis, we used the lme4 package implementation in R (Bates et al., 2008) to fit the generalized mixed effects model. The specification for the full model is as follows: −1
Pr(yi = 1) = logit j ∼N(0 , 2 )
P(Y = 1) P(Y = 0)
2.3.3. Overfitting and multicollinearity. In order to address the issue of overfitting problem, we validated our results with the receiver-operating characteristics (ROC) with cross-validation (Bradley, 1997; Fawcett, 2006). We used cross-validation with 1000 iterations to determine the mean AUC and the associated 95% confidence interval for each model specification (area-under-curve (AUC) of less than 0.7 is considered to be indicative of overfitting problem.) We also tested for multi-collinearity by estimating the variance-inflation-factor (VIF) of the coefficients and found that these are less than the critical threshold of 5.
(j + sj[−i] + Xi,selection + Xi,influence +sj[−i] ×Xi,selection + sj[−i] ×Xi,influence + Xi,covariates )
where i = 1, . . . , N individuals and j = 1, . . . , J schools in the sample.
(1)
yi is the smoking initiation (1 = Yes; 0 = No) indicator, qj is the varying school intercept, sj[−i] is the proportion of consistent nonsmokers in the school (excluding those who lay in the individual i’s social space), and Xselection and Xinfluence are the sets of influence and selection variables of interest, respectively. sj[−i] × Xselection and sj[−i] × Xinfluence are the respective sets of interactions with the school social norm variable. Finally, Xcovariates is the set of demographic and personal characteristic variables. The aim of the propensity scores is to make balanced comparison between the treatment and comparison groups as in a randomized controlled trial, albeit with observational data (Rosenbaum and Rubin, 1983). Here “treatment” denotes the presence of characteristics or behaviors of interest, which in this study is smoking initiation. Balancing involves creating weights that quantifies the comparison group’s similarity to the “treatment” group. These probability weights, also called propensity score weights, are estimated with the boosting technique (McCaffrey et al., 2004). In the present context propensity scores attribute more importance to the cases that do not report having smoking peers yet are similar to smokers in their background characteristics (Ridgeway, 2006; Morral et al., 2004). log
new smoking friends 2–3 degrees away > 0; 0 otherwise) was chosen as the “treatment” group, since the cases that correspond to this particular combination of effects are the ones that are most interesting in terms of policy. We obtained the propensity score weights in the form of odds by running generalized boosted regression of treatment assignment on the set of covariates, using the gbm package in R (Ridgeway, 2007). Since the use of propensity weights can bias the error estimates, sandwich estimators (Zeileis, 2004) for the variance in the weighted propensity score model were used to obtain robust standard errors.
= ˇ0 + T1 + T2 + T3 +
K
ˇk Xk
(2)
k=1
Because there are three vectors of “treatment” (number of former, same, and new friends for each degree of separation) for selection, de-selection, and influence effects, it was not feasible to apply the usual application of propensity score modeling, which only allows binary categorization. We instead built a multiindicator propensity score model with three indicators, in which T1 , T2 , T3 are binary indicators that stand for selection, de-selection, and influence effects respectively (three social distance vectors were summarized into one single binary indicator for each of the effects). The combination of positive selection, no de-selection, and positive influence (T1 = 1 if the number of new smoking friends initially >3 degrees away > 0; 0 otherwise; T2 = 0 (1 if the number of former smoking friends 2 or more degrees away > 0; 0 otherwise); T3 = 1 if the number of consistent smoking friends OR the number of
3. Results 3.1. Peer effects on smoking initiation Results from the mixed effects regression shown in Table 2 indicate a significant direct influence effect on smoking initiation (Model 1): each consistent smoking friend who is 1 degree away increases the likelihood of an adolescent starting to smoke by 80% (OR = 1.79, 95% CI = 1.38, 2.34). After adding the selection and de-selection variables (Model 2), this direct influence effect remains essentially unchanged. Model 2 also shows evidence of both a selection and de-selection effect on smoking initiation. Selecting a new smoking friend who was initially more than three degrees away from the focal adolescent (OR = 2.08, 95% CI = 1.86, 2.93), befriending a friend’s smoking friend (two degrees away; OR = 1.63, 95% CI = 1.03, 2.59), and de-selecting a smoking friend (more than three degrees away; OR = 1.29, 95% CI = 1.02, 1.64) are all associated with an increased likelihood of smoke uptake. When the school norm × peer effect interaction terms are added to the model (Model 3), the two-degree selection effect and the de-selection effect are no longer significant, implying that their positive results were actually due to the significant presence of smokers at the school. The selection and direct influence effects remain significant, however. The addition of new smoking peers from three degrees away more than doubles the likelihood that the adolescent starts smoking (OR = 2.18, 95% CI = 1.27, 3.76), and having a consistent smoking friend also more than doubles the odds of smoking uptake (OR = 2.40, 95% CI = 1.14, 5.06). While the addition of interaction terms decreases the direct influence effect (OR increases from 1.79 to 2.40), it does not reduce the selection effect as much (OR increases from 2.08 to 2.18). This seems to indicate that the direct influence effect is more affected by the level of school social norm towards smoking (the corresponding interaction term is negative in logit coefficient but not significant) than is the selection effect. 3.2. Other predictors of smoking initiation Across the three models shown in Table 2, the same covariates emerge as significant predictors of smoking initiation.
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
351
Table 2 Multilevel models predicting smoking initiation from indicators of peer selection, peer influence, school social norm, and covariates. Variable
OR School social norm % consistent non-smokers at the school 0.66 Indicator of peer selection # new smoking friends initially >3 degrees away # new smoking friends initially 3 degrees away # new smoking friends initially 2 degrees away Peer de-selection # former smoking friends now >3 degrees away # former smoking friends now 3 degrees away # former smoking friends now 2 degrees away Indicator of direct peer influence 1.80 # of consistent smoking friends 1 degree away Indicators of indirect peer influence 0.94 # consistent smoking peers 2 degrees away 1.03 # consistent smoking friends 3 degrees away School norm × peer effect interactions School norm × selection (>3 degrees) School norm × de-selection (>3 degrees) School norm × selection (3 degrees) School norm × de-selection (3 degrees) School norm × selection (2 degrees) School norm × de-selection (2 degrees) School norm × direct influence (1 degree) School norm × indirect influence (2 degrees) School norm × indirect influence (3 degrees) Background characteristics 0.97 Gender: female Grade in school (reference group: grade 7) 1.70 Grade 8 1.62 Grade 9 1.19 Grade 10 1.34 Grade 11 0.87 Grade 12 Race/ethnicity (reference group: White) 0.45 African American Asian 0.88 1.04 Hispanic Parent education (reference group: less than HS) 1.23 High school graduate/GED 1.11 Some college 0.76 College degree or higher 1.06 Depression Grade point average 0.68 Self esteem 1.00 0.86 School attachment Delinquency 3.41 Mother smokes 1.06 1.78 Father smokes ROC 0.69 Area under curve (AUC) * ** ***
Influence + selection (Model 2)
Influence (Model 1) 95% CI
Sig.
(0.20, 2.17)
OR
95% CI
0.86
(0.29, 2.54)
Full model (Model 3) Sig.
***
OR
95% CI
0.93
(0.27, 3.23)
2.18
(1.27, 3.76)
5.55
(0.68, 45.33)
2.08
(1.68, 2.58)
1.56
(0.87, 2.79)
1.63
(1.03, 2.59)
*
1.60
(0.54, 4.68)
1.29
(1.02, 1.64)
*
1.04
(0.51, 2.14)
1.16
(0.55, 2.42)
0.91
(0.10, 7.90)
1.06
(0.63, 1.77)
0.36
(0.07, 1.97)
1.79
(1.38, 2.34)
2.40
(1.14, 5.06)
(0.73, 1.21)
0.84
(0.64, 1.11)
0.59
(0.25, 1.38)
(0.91, 1.16)
1.02
(0.89, 1.17)
1.35
(0.65, 2.82)
(1.39, 2.33)
***
***
0.89 1.99 0.02 2.24 1.14 33.1 0.47 3.09 0.37
(0.03, 4.83)
0.95
(0.70, 1.29)
0.94
(0.69, 1.28)
(0.85, 3.38) (0.76, 3.46) (0.60, 2.35) (0.67, 2.71) (0.34, 2.23)
1.57 1.15 1.09 1.17 0.81
(0.79, 3.13) (0.53, 2.49) (0.57, 2.10) (0.60, 2.28) (0.32, 2.05)
1.54 1.12 1.06 1.12 0.78
(0.77, 3.07) (0.51, 2.44) (0.55, 2.06) (0.57, 2.21) (0.31, 1.98)
0.49 0.81 1.03
(0.27, 0.89) (0.48, 1.38) (0.60, 1.76)
0.49 0.79 1.02
(0.27, 0.89) (0.46, 1.36) (0.60, 1.75)
1.26 1.17 0.83 1.06 0.71 1.01 0.81 3.04 0.99 1.89
(0.75, 2.12) (0.66, 2.09) (0.46, 1.48) (1.00, 1.11) (0.57, 0.89) (0.78, 1.31) (0.66, 1.00) (2.03, 4.56) (0.73, 1.36) (1.39, 2.57)
1.26 1.16 0.82 1.06 0.71 1.01 0.81 2.96 0.98 1.92
(0.75, 2.12) (0.65, 2.06) (0.46, 1.47) (1.01, 1.11) (0.57, 0.90) (0.78, 1.31) (0.65, 1.00) (1.97, 4.44) (0.72, 1.35) (1.41, 2.62)
0.71
(0.67, 0.74)
0.71
(0.66, 0.74)
(0.74, 2.04) (0.63, 1.95) (0.43, 1.36) (1.01, 1.11) (0.54, 0.84) (0.78, 1.29) (0.69, 1.05) (2.30, 5.05) (0.78, 1.44) (1.32, 2.41)
*
* ***
* ***
***
(0.66, 0.71)
*
* **
* ***
***
***
*
(0.26, 3.03) (0.23, 17.08) (0.00, 12.65) (0.00, 1629) (0.07, 19.30) (0.19, 5750) (0.07, 3.10) (0.26, 37.3)
(0.71, 1.31)
(0.25, 0.83) (0.49, 1.58) (0.58, 1.86)
Sig.
*
* **
* ***
***
p < 0.05. p < 0.01. p < 0.00.
Initiation is less likely among adolescents who are African American (compared to non-Hispanic white), have a higher grade point average, and have a stronger school attachment. Adolescents are more likely to start smoking if they have more depressive symptoms, greater involvement in delinquency, and a father who smokes.
3.3. Results from propensity score model Fig. 2 shows the graph of Kolmogorov–Smirnov statistics (showing that the difference in the underlying distribution between the treatment and comparison groups is minimized (towards the center-line) when the comparison group is weighted with the
352
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Fig. 2. Propensity score balance check: Kolmogorov–Smirnov statistics.
propensity scores (“+”)), indicating that the unweighted averages of the covariates differ substantially between the comparison and treatment groups, but with the application of propensity score weights these differences diminish substantially for most variables. This demonstrates that the propensity score adjustment reduces pre-existing background differences between groups. Results of the propensity score model are consistent with the pattern of results from the mixed effect regression models (Table 3). In terms of peer effects, the selection effect adjusted with the propensity score weights is significantly associated with smoking initiation (OR = 3.04, 95% CI = 2.18, 4.22), as well as the direct influence effect (OR = 1.91, 95% CI = 1.33, 2.75). The addition of interaction terms in Model 5 shows that the effect of selection is virtually the same as in Model 4 (OR = 3.04, 95% CI = 1.22, 7.58), but the influence effect increases the odds by 80% from Model 4 (OR = 3.46, 95% CI = 1.17, 10.28). This change is accompanied by stronger (negative) interaction effect (OR = 0.24, 95% CI = 0.02, 3.19), however. In contrast the corresponding coefficient for the interaction effect of selection is unity. The propensity score model confirms findings from the mixed effects model, suggesting that influence effects are more sensitive than selection effects to school social norms. As for overfitting, the propensity score weights model with its simplified binary specification has an AUC of 0.74 (95% CI = 0.70, 0.78), which is an improvement over the regression models (AUC = 0.69–0.71). Both approaches are therefore internally consistent and should
be generalizable to other adolescent populations in similar school settings. 4. Discussion Adolescents surround themselves with peers of similar backgrounds characteristics (de Leeuw et al., 2008; Kandel, 1978), and some of these characteristics are possibly susceptible to the contextual factors that create a favorable environment for smoking. In this study we applied the concept of social distance in the context of differentiating selection and influence effects on the problem of adolescent smoking initiation. Our study finds compelling evidence for a selection effect on smoking and a direct influence effect of the immediate peers (Engels et al., 1997; Krauth, 2005; Wang et al., 2000), suggesting that adolescent smoking initiation is closely intertwined with personal dynamics, while discounting the role of group dynamics such as cliques in influencing adolescents’ smoking behavior. While results suggest that selection and direct influence effects are both important drivers of smoking initiation, their modus operandi is different. The strength of direct peer influence seems related to the prevailing school social norms towards smoking, with the impact of direct peer influence being stronger at schools with a higher prevalence of smoking. In contrast, the strength of peer selection (i.e., selecting smoking friends more than three degrees
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
353
Table 3 Propensity score weighted logistic regression (Models 4 and 5). Variable
School social norm % consistent non-smokers at the school Indicators of: Selection De-selection Influence School norm × peer effect interactions School norm × selection School norm × de-selection School norm × influence Background characteristics Gender: female Grade in school (reference group: grade 7) Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 Race/ethnicity (reference group: White) African American Asian Hispanic Parent education (reference group: less than HS) High school graduate/GED Some college College degree or higher Depression Grade point average Self esteem School attachment Delinquency Mother smokes Father smokes ROC Area under curve (AUC) * ** ***
Full model (Model 5)
Influence + selection (Model 4) OR
95% CI
0.89
(0.30, 2.69)
3.04 1.39 1.91
(2.18, 4.22) (0.91, 2.11) (1.33, 2.75)
Sig.
***
***
OR
95% CI
0.82
(0.21, 3.25)
3.04 1.04 3.46
(1.22, 7.58) (0.51, 2.14) (1.17, 10.28)
1.03 15.08 0.24 (0.68, 1.29)
0.92
(0.67, 1.27)
1.60 1.31 1.20 1.27 0.88
(0.76, 3.36) (0.61, 2.83) (0.59, 2.44) (0.62, 2.58) (0.34, 2.31)
1.64 1.39 1.22 1.28 0.90
(0.77, 3.49) (0.64, 3.05) (0.59, 2.51) (0.62, 2.64) (0.34, 2.41)
0.49 0.75 1.00
(0.27, 0.87) (0.43, 1.28) (0.58, 1.73)
0.46 0.69 0.97
(0.25, 0.83) (0.40, 1.20) (0.56, 1.67)
1.42 1.21 0.89 1.05 0.67 1.00 0.83 3.02 1.06 1.79
(0.85, 2.38) (0.69, 2.12) (0.50, 1.58) (1.00, 1.11) (0.50, 0.90) (0.78, 1.30) (0.68, 1.00) (2.00, 4.55) (0.77, 1.46) (1.31, 2.45)
1.47 1.24 0.91 1.06 0.67 1.00 0.83 2.96 1.08 1.82
(0.88, 2.46) (0.7, 2.18) (0.51, 1.62) (1.00, 1.12) (0.50, 0.90) (0.77, 1.29) (0.68, 1.01) (1.95, 4.48) (0.78, 1.49) (1.33, 2.49)
0.74
(0.71, 0.77)
0.74
(0.70, 0.78)
* **
* ***
***
*
*
(0.14, 7.71) (0.89, 255.95) (0.02, 3.19)
0.94
*
Sig.
**
* **
***
***
p < 0.05. p < 0.01. p < 0.00.
away) seems indifferent to school social norms towards smoking, suggesting that this effect may primarily be driven by the adolescent smoker’s need for homophily. In addition, the fact that indirect peer effects are significant only when they are not interacted with the school social norm shows that indirect effects are actually indistinguishable from the effects of larger contextual factors. This contrasts with the 3-degree “induction” (i.e., influence) effect found by Christakis and Fowler (2007, 2008) using Framingham Heart Study data, which suggested that the influence of peers may be found for up to three degrees of separation (i.e., “friends of friends of friends”). In contrast to the direct influence effect, interpretation of the significant selection effect should be more nuanced as we cannot determine whether smoking initiators select smoking peers first and then start smoking, or take up smoking first and select smoking peers based on shared preferences (our definition of selection therefore includes both). Further, factors other than smoking that attract adolescents to their smoking peers (e.g., risky behaviors, physical attraction) are potential confounders of the selection effect (Abel et al., 2002; Ennett and Bauman, 1993; Guo, 2006; Scarr and McCartney, 1983). We addressed the issue of potential confounders with the propensity score weights, and tested for overfitting and multi-collinearity with ROC and VIF, respectively, to address potential concerns on the complexity in model specifications. Results from this study suggest that adolescent smoking homophily is due to the combined effect both of being exposed to smoking peers and actively exercising friendship choice. Our
study shows that smoking initiation among adolescents occurs as part of a close personal interaction, either between existing friends or in the process befriending new friends outside of one’s social space. In addition, we find that “friends of friends” effects are indistinguishable from the broader environmental factors that influence adolescents. Adolescent drug prevention programs typically place a strong emphasis on peer pressure and how to resist it. Our results suggest that it is also important to recognize that: (1) adolescents actively choose friends as well as (risky) behaviors consistent with their substance use and friendship preferences; and (2) the smoking risk to which adolescents are exposed can be ameliorated by targeting the social acceptance of smoking at large. While the effectiveness of prevention programs may be limited by the social dimension of smoking that closely intertwines smoking with friendship, they may be enhanced by targeting the social acceptance of smoking among adolescents.
Role of funding source Funding for this study was provided by grant 16RT-0169 from the California Tobacco-Related Disease Research Program (TRDRP) of the University of California, awarded to Dr. Tucker as Principle Investigator. TRDRP had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
354
M.-H. Go et al. / Drug and Alcohol Dependence 124 (2012) 347–354
Contributors All authors have been personally and actively involved in substantive work leading to the report, and will hold themselves jointly and individually responsible for its content. Conflict of interest None declared. Data source This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with Cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123W. Franklin Street, Chapel Hill, NC 27516-2524, United States ([email protected]). Connection with tobacco, alcohol, pharmaceutical or gaming industry: None. References Abel, G., Plumridge, L., Graham, P., 2002. Peers, networks or relationships: strategies for understanding social dynamics as determinants of smoking behaviour. DrugEdu. Prev. Policy 9, 325–338. Alexander, C., Piazza, M., Mekos, D., Valente, T., 2001. Peers, schools, and adolescent cigarette smoking. J. Adolesc. Health 29, 22–30. Bates, D., Maechler, M., Dai, B., 2008. lme4: Linear Mixed-effects Models Using S4 Classes., http://lme4.r-forge.r-project.org/. Bearman, P.S., Jones, J., Udry, J.R., 1997. The national longitudinal study of adolescent health: research design http://www.cpc.unc.edu/projects/addhealth/design (retrieved February 2003). Bradley, A., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30, 1145–1159. Calvó-Armengol, A., Patacchini, E., Zenou, Y., 2005. Peer effects and social networks in education and crime. C.E.P.R. Discussion Papers http://ideas.repec.org/p/cpr/ceprdp/5244.html (accessed January 2010). Chen, X., Stanton, B., Fang, X., Li, X., Lin, D., Zhang, J., Liu, H., Yang, H., 2006. Perceived smoking norms, socioenvironmental factors, personal attitudes and adolescent smoking in China: a mediation analysis with longitudinal data. J. Adolesc. Health 38, 359–368. Christakis, N.A., Fowler, J.H., 2007. The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357, 370–379. Christakis, N.A., Fowler, J.H., 2008. The collective dynamics of smoking in a large social network. N. Engl. J. Med. 358, 2249–2258. de Leeuw, R., Scholte, R., Harakeh, Z., van Leeuwe, J., Engels, R., 2008. Parental smoking-specific communication, adolescents smoking behavior and friendship selection. J. Youth Adolesc. 37, 1229–1241. Dishion, T.J., McCord, J., Poulin, F., 1999. When interventions harm: peer groups and problem behavior. Am. Psychol. 54, 755–764. Dornbusch, S., Erickson, K., Laird, J., 2001. The relation of family and school attachment to adolescent deviance in diverse groups and communities. J. Adolesc. Res. 16, 396–422. Engels, R., Knibbe, R.A., Drop, M.J., deHaan, Y.T., 1997. Homogeneity of cigarette smoking within peer groups: influence or selection? Health Educ. Behav. 24, 801–811. Ennett, S.T., Bauman, K.E., 1993. Peer group structure and adolescent cigarette smoking: a social network analysis. J. Health Soc. Behav. 34, 226–236.
Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874. Flay, B.R., Hu, F.B., Richardson, J., 1998. Psychosocial predictors of different stages of cigarette smoking among high school students. Prev. Med. 27, A9–A18. Go, M.-H., Green Jr., H.D., Kennedy, D.P., Pollard, M., Tucker, J.S., 2010. Peer influence and selection effects on adolescent smoking. Drug Alcohol Depend. 109, 239–242. Guo, G., 2006. Genetic similarity shared by best friends among adolescents. Twin Res. Hum. Genet. 9, 113–121. Hall, J.A., Valente, T.W., 2007. Adolescent smoking networks: the effects of influence and selection on future smoking. Addict. Behav. 32, 3054–3059. Honaker, J., King, G., Blackwell, M., 2008. Amelia: Amelia II: A Program for Missing Data., http://gking.harvard.edu/amelia (accessed January 2010). Kandel, D.B., 1978. Homophily, selection, and socialization in adolescent friendships. Am. J. Sociol. 84, 427. King, G., Honaker, J., Joseph, A., Scheve, K., 2001. Analyzing incomplete political science data: an alternative algorithm for multiple imputation. Am. Polit. Sci. Rev. 95, 49–69. Kirke, D.M., 2004. Chain reactions in adolescents’ cigarette, alcohol and drug use: similarity through peer influence or the patterning of ties in peer networks? Soc. Networks 26, 3–28. Kobus, K., 2003. Peers and adolescent smoking. Addiction 98, 37–55. Krauth, B.V., 2005. Peer effects and selection effects on smoking among Canadian youth. Can. J. Econ. 38, 735–757. Leatherdale, S.T., Brown, K.S., McDonald, P.W., Cameron, R., 2005a. Social modeling in the school environment, student characteristics, and smoking susceptibility: a multi-level analysis. J. Adolesc. Health 37, 330–336. Leatherdale, S.T., McDonald, P.W., Cameron, R., Brown, K.S., 2005b. A multilevel analysis examining the relationship between social influences for smoking and smoking onset. Am. J. Health Behav. 29, 520–530. Mariano, K.A., Harton, H.C., 2005. Similarities in aggression, inattention/hyperactivity, depression, and anxiety in middle childhood friendships. J. Soc. Clin. Psychol. 24, 471–496. McCaffrey, D.F., Ridgeway, G., Morral, A.R., 2004. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol. Methods 9, 403–425. Mercken, L., Snijders, T., Candel, M., Steglich, C., De Vries, H., 2005. Why adolescents start to smoke: peer influence versus peer selection processes. Psychol. Health 20 (Supp. 1), 170. Morral, A.R., McCaffrey, D.F., Ridgeway, G., 2004. Effectiveness of communitybased treatment for substance-abusing adolescents: 12-month outcomes of youths entering Phoenix Academy or alternative probation dispositions. Psychol. Addict. Behav. 18, 257–268. Perreira, K., Deeb-Sossa, N., Harris, K., 2005. What are we measuring-an evaluation of the CES-D across race/ethnicity and immigrant generation. Soc. Forces 83, 1567–1602. Ridgeway, G., 2006. Assessing the effect of race bias in post-traffic stop outcomes using propensity scores. J. Quant. Criminol. 22, 1–29. Ridgeway, G., 2007. gbm: Generalized Boosted Regression Models., http://www.ipensieri.com/gregr/gbm.shtml (accessed January 2010). Rosenbaum, P.R., Rubin, D.B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55. Rosenquist, J.N., Murabito, J., Fowler, J.H., Christakis, N.A., 2010. The spread of alcohol consumption behavior in a large social network. Ann. Intern. Med. 152, 426–433. Rubin, D.B., Thomas, N., 1996. Matching using estimated propensity scores: relating theory to practice. Biometrics 52, 249–264. Scarr, S., McCartney, K., 1983. How people make their own environments: a theory of genotype, environment effects. Child Dev. 54, 424–435. Schafer, J., 1997. Analysis of Incomplete Multivariate Data. Chapman and Hall/CRC, London. Snijders, T., Pearson, M., 2010. Dynamic networks and behavior: separating selection from influence. Soc. Methodol. 40, 329–393. Snijders, T., 2001. The statistical evaluation of social network dynamics. Soc. Methodol. 31, 361–395. Urberg, K., De˘girmencio˘glu, S.M., Pilgram, C., 1997. Close friend and group influence on adolescent cigarette smoking and alcohol use. Dev. Psychol. 33, 834–844. Wang, M.Q., Eddy, J.M., Fitzhugh, E.C., 2000. Smoking acquisition: peer influence and self-selection. Psychol. Rep. 86, 1241–1246. Zeileis, A., 2004. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 10, 1–17.