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Universality of Effects: An Examination of the Comparability of Long-Term Family Intervention Effects on Substance Use Across Risk-Related Subgroups

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Abstract

This study extends earlier investigation of family risk-related moderation of two brief, family-focused preventive interventions. It examines effects on the trajectories of substance initiation over a period of six years after a pretest assessment, evaluating whether effects were comparable across higher- and lower-risk subgroups. The two interventions, designed for general-population families of adolescents, were the seven-session Iowa Strengthening Families Program (ISFP) and the five-session Preparing for the Drug Free Years program (PDFY). Thirty-three rural public schools were randomly assigned to either the ISFP, the PDFY, or a minimal contact control condition. Curvilinear growth curve analyses were used to evaluate the universality of intervention effectiveness by testing for risk moderation of intervention effects on school-level substance use trajectories of initiation of alcohol and illicit substance use. Results were most consistent with the interpretation that both interventions provided comparable benefits for both outcome measures, regardless of family risk status. Findings are discussed in terms of their implications for implementing universal preventive interventions in general populations.

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Notes

  1. For purposes of this study, analyses examining differential attrition across experimental conditions were extended to the 12th-grade follow-up assessment. Two 2 (Condition: Intervention vs. Control) × 2 (Attrition status: Attritted vs. Retained) analyses of variance (ANOVA) were conducted for each outcome measure, with one ANOVA focusing on PDFY-control comparisons and the other ANOVA focusing on ISFP-control comparisons. No significant Condition × Attrition status interaction effects were found for either of the outcome variables between the 6th grade pretest and the 12th-grade follow-up. At each of the six waves of data collection, the numbers of participating families who had also completed all possible preceding assessments from posttest through the final assessment were 551, 458, 419, 374, and 344, respectively, which corresponds to an average wave-to-wave retention rate of 87.7%.

  2. Over the six year course of the study, 28% of all respondents (23% ISFP, 35% PDFY, 27% control) made an inconsistent report on at least one of the three lifetime alcohol use items; 4% of all respondents (4% ISFP, 5% PDFY, 3% control) made an inconsistent report on at least one of the two lifetime illicit substance use items.

  3. Although inconsistent with assumptions for the specific statistical model used to analyze the reported results, parallel, exploratory analyses were conducted in which individual initiation scores were allowed to freely change across data collection waves. These analyses were performed to help gauge the robustness of the findings reported. The pattern of statistically significant results remained unchanged (see also Spoth, Redmond, & Shin, 2001).

  4. For the purpose of our power analyses for both measures we used a minimal difference of 12-months of delay in initiation between the higher- and lower-risk subgroups as our criterion. We selected this criterion because there is a case to be made that a difference of less than 12 months would be of questionable practical significance. This conclusion is based on consideration of prevalence data from Grant and Dawson (1997) that links age of initiation during adolescence to the development of alcohol use disorders in adulthood. A 12-month difference between higher- and lower-risk groups would correspond to only about a 3% difference in the risk associated with developing an alcohol use disorder in the future for the current sample. It should also be noted that a 12 month difference represents only about half of the intervention effect when ignoring risk grouping on most initiation measures. We calculated the results of the school-level power analyses on the alcohol initiation measure for the range of 12–18 months of initiation delay. In the one-tailed case for ISFP they range from .75 to .95 and, for PDFY, they range from .68 to .93. Power was markedly lower for analyses focusing on illicit substance initiation, ranging from .16 to .25 for ISFP, and from .18 to .29 for PDFY.

    We considered the possibility of conducting analyses at the individual-level in order to attain greater power. However, conducting individual-level analyses was not desirable in this case, for two reasons. First, individual-level responses on the outcome variables were quite discontinuous in shape across time. That is, individual trajectories of use would often “jump” abruptly from one assessment to the next. Thus, the individual-level outcome data are not well described by growth curve trajectories in general, nor by the logistic growth curve used in the current analysis. As a result, the model provided a poor fit to the individual-level data. By contrast, school-level responses are formed by averaging the individual-level responses, a procedure that produces trajectories that are more continuous across time and better described by growth curve modeling. Second, the individual-level analyses actually possessed less statistical power due to the fact that there was much greater variation among individual-level responses than among school-level responses. That is, even though the individual-level analyses did tend to benefit power insomuch as they increased degrees of freedom, this gain was more than offset by the greater imprecision of the parameter estimates.

  5. More general results associated with the fitting of logistic growth curves to initiation outcome measures through six years following pretest—regardless of family risk status—are provided by Spoth, Redmond, Shin, and Azevedo (2004). To avoid undue lengthiness and to maintain focus on evaluating intervention-control differences in the context of lower- and higher-risk families, we do not reproduce those more general results, and instead refer the interested reader to the cited report.

  6. In comparison to linear trajectory models, greater care is required to correctly interpret the meaning of the parameters of S-shaped nonlinear trajectory models. For example, the lack of statistically significant intervention-control differences on parameters associated with maximum values (as captured by the parameters, α P and α S ) should not be interpreted as meaning that there were no intervention-control differences in the levels of substance use at the 12th grade (the final wave of data assessment). Rather, null findings for should be interpreted as meaning that the estimated growth trajectories of the two conditions will ultimately–at some later point in time–approach maximum limits that are not statistically different from each other. If one wishes to test for intervention and risk status effects on a particular outcome, it is necessary to appropriately adapt the traditional growth curve model, as described in the Appendix.

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Acknowledgements

Work on this article was supported by National Institute on Drug Abuse Grant DA 07029, by National Institute of Mental Health Grant MH 49217 and by the National Institute on Alcohol Abuse and Alcoholism Grant AA 014702. The authors gratefully acknowledge the valuable comments and editorial assistance of Kevin Haggerty, J. David Hawkins, Richard Kosterman, W. Alex Mason, and Linda Trudeau in the preparation of this manuscript.

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Appendix

Appendix

Curvilinear logistic growth curve models were applied to school-level alcohol and illicit substance initiation outcomes in order to investigate effects of the intervention, risk status (higher and lower), and interaction effects between intervention condition and risk status. Analyses were conducted by using the following mathematical model to fit the data:

$$ y_{ijk} = \frac{{A + U_{ij} }}{{1 + {\rm exp}( - B)}} + e_{ijk}, $$
$$\displaylines{{\rm A} = \alpha_0 + \alpha_P P_i + \alpha_S S_i + \alpha_R R_j + \alpha_{\rm PR} P_i R_j + \alpha_{\rm SR} S_i R_j ,\cr {\rm B} = \beta_0 + \beta_P P_i + \beta_S S_i + \beta_R R_j + \beta_{\rm PR} P_i R_j + \beta_{\rm SR} S_i R_j \cr +\,\,( {\gamma_0 + \gamma_P P_i\! + \gamma_S S_i\! + \gamma_R R_j + \gamma_{\rm PR} P_i R_j\! + \gamma_{\rm SR} S_i R_j }) \cr \times\,\, t_{ijk} }$$

where, i=1, 2, … , 52 (school-level risk groups as units of analysis), j=1, 2 (risk status; high vs. low), and k=1, 2, … , 6 (waves of data collection). In this model, y ijk is the school-level outcome variable (averaged across all students in each school in each treatment condition and risk group at the kth wave of data collection) and t ijk is the average number of months (for each school in each treatment condition and risk group) that had elapsed between the pretest assessment and the assessment at wave k. The intervention condition is indicated by two dummy variables, P i and S i . P i is a dichotomous variable that equals 1 for the PDFY intervention condition and 0 for the other conditions. S i is also a dichotomous variable that equals 1 for the ISFP intervention condition and 0 for the other conditions. The risk group is represented by R j , a dichotomous variable that equals 0 for the lower-risk group and 1 for the higher-risk group. The e ijk are random errors associated with the individual units of analysis that are assumed to be independent and normally distributed with mean zero and variance σ2. The U ij are included to model random error in the school-level growth curves, the random effects of which are assumed to be independently, identically, and normally distributed with mean zero and variance \(\sigma_s^2\) and to be independent of the e ijk (Lindstrom and Bates, 1990).

In the model, the parameters describing the growth curves for the reference group (i.e., the lower-risk group in the control condition) are α 0, β 0, and γ 0. The intervention main effects are indicated by the values of α P , α S , β P , β S , γ P , and γ S . The risk status main effects are indicated by α R , β R , and γ R . The risk × intervention interaction effect is captured by the parameters α PR , α SR , β PR , β SR , γ PR , and γ SR . Because they are coefficients of t ijk , the γ parameters primarily reflect the rate of increasing initiation over time. Accordingly, they have a large effect on the shape of the substance use trajectory estimated by the model. The upper limit or maximum value of the curve occurs at the latest point in the trajectory, that is, when t ijk (i.e., the amount of time that has passed since the pretest assessment) becomes very large, which causes the denominator of the above mathematical expression to approach unity. Hence, the maximum value that the growth curve should ultimately approach is determined by the α parameters. By contrast, the lower-limit of the growth curve defined by the model corresponds to the value when \(t_{ijk} = 0\), at the time of the pretest assessment. At this point, the β parameters are most influential, and so primarily reflect differences relating to initial substance initiation levels.

The model presented in the above equation can be adapted in order to test for intervention, risk, and risk × intervention effects on characteristics of the substance initiation trajectories at particular points in time by using the ESTIMATE statement of the SAS PROC NLMIXED procedure. For example, time (months after the pretest) to reach a particular level p in an outcome variable for the higher risk group of the ISFP intervention condition is:\( - \frac{{\{ {( {\beta _0 + \beta _S + \beta _R + \beta _{SR} } ) + {\rm log}[ { ( {{\textstyle{{\alpha _0 + \alpha _S + \alpha _R + \alpha _{SR} } \over p}}} ) - 1} ]} \}}}{{( {\gamma _0 + \gamma _S + \gamma _R + \gamma _{SR} } )}}.\)

The results of testing risk moderation reported in Table 6 are from applications of such computations.

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Spoth, R., Shin, C., Guyll, M. et al. Universality of Effects: An Examination of the Comparability of Long-Term Family Intervention Effects on Substance Use Across Risk-Related Subgroups. Prev Sci 7, 209–224 (2006). https://doi.org/10.1007/s11121-006-0036-3

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