Culturally Adapted Brief Intervention for Heavy Drinking Hispanic Men

Non-Adapted Brief Motivational Intervention: The core components of NA-BMI are consistent
with the person-centered approach of MI and include 1) providing personalized feedback based
on screening and baseline assessment results; 2) exploring decisional balance (pros and
cons) of alcohol use from the patient's perspective; 3) building motivation for change
through the assessment and discussion of the patients' selfreport of levels of importance,
confidence, and readiness to change drinking; 4) enhancing commitment to change by exploring
the patient's options for change and developing a change plan if indicated or desired; and
5) providing referrals for formal treatment of alcohol problems and other community
resources. The NA-BMI will not specifically target cultural risk or protective factors
beyond any normal tailoring that may occur in a standard BMI as described in the current
literature. In NA-BMI, personalized feedback will be based on drinking norms and frequency
of alcohol problems from the U.S. general population.

Culturally Adapted Brief Motivational Intervention: CA-BMI also adheres to the core
principles of MI and practice of BMI. In CA-BMI, the core components of NA-BMI are adapted
to be culturally responsive to the unique risk (acculturative stress) and protective
(familism) factors associated with heavy drinking, alcohol problems, help seeking, and
treatment utilization among Latinos. It is important to note that CA-BMI goes well beyond
any tailoring that may occur in NA-BMI by targeting factors that are important predictors of
drinking among Latinos. Specifically, there are two primary adaptations to the CA-BMI:

1. CA-BMI will incorporate the assessment and personalized feedback on the impact of
acculturative stress on drinking so as to decrease temptation to drink and increase
confidence to avoid drinking. Specifically, participants will receive feedback about
the types and intensity of acculturative stress they may experience (e.g., issues
related to immigration, cultural congruity, language barriers, and employment
discrimination), and clinicians will elicit the relationship of acculturative stress to
temptation and confidence to avoid drinking.

2. CA-BMI will also integrate family and community as reasons for change and as agents of
behavior change when considering the impact of drinking, plans for changing drinking
behavior, and engagement in help-seeking behaviors. Following methods developed by Lee
et al. (2011) and Añez et al. (2008), consultants on the grant, investigators will
incorporate a discussion of how social context and family dynamics are related to
drinking.

These modifications result in a culturally adapted intervention that is substantially
distinct in its content and focus (e.g., deep structural changes) from a non-adapted
intervention, while maintaining consistency with motivational interviewing and its
application in brief alcohol intervention. In accord with the two central adaptations,
investigators anticipate that the potential mediators or mechanisms of behavior change
specific to CA-BMI are 1) temptation to drink and confidence to avoid drinking and 2)
increased support from family and friends in general as well as specific support to change
drinking behavior and seek treatment. Finally, investigators will also evaluate a definition
of treatment utilization that is more comprehensive than that in the investigators prior
study, which assessed the use of formal inpatient and outpatient substance abuse treatment
and attendance to self-help groups such as Alcoholics Anonymous (Field, et al., 2010). In
the current study, investigators will assess engagement in formal treatment networks as well
as informal help-seeking common among Latinos (e.g., seeking help from family, religious
leaders, or respected elders in the community).

Statistical Analyses Preliminary Analyses: Standard examinations for outliers, data
distribution, and internal consistency of measures will be conducted. For mixed models,
investigators will assess the homogeneity of error and normality of residuals at all levels
of the model, test for multivariate normality of random effects, examine linearity, and
identify outliers. For structural equation models (SEM), investigators will follow the best
practice guidelines outlined by Boomsma (2000) for analyzing and reporting SEM models.
Investigators will also compare groups on all demographic and pretest variables to assess
whether randomization produced equivalent groups; in the event of nonequivalent variables,
these variables will be included as covariates in models.

Data Analysis for Specific Aim 1: Analyses investigating group differences in alcohol
problems and treatment utilization will use random coefficient models (Raudenbush & Bryk,
2002; Singer & Willett, 2003). Investigators will construct longitudinal models using the
following sequence of analytic steps recommended by Singer and Willett (2003): 1) examine
empirical growth plots; 2) fit an unconditional means model; 3) fit an unconditional linear
growth model; 4) fit an unconditional non-linear model (e.g., piecewise model); 5) determine
the best model of longitudinal change by comparing models in the previous two steps using
the Akaike information criterion (AIC); (f) select the most appropriate error covariance
structure using AIC; and 6) add level-2 predictors (e.g., intervention conditions). Models
for binary outcomes (e.g., treatment utilization) will use generalized linear mixed-effects
models assuming a binary distribution with a logistic link function.

Data Analysis for Specific Aim 2: Potential moderators will be examined by constructing
interaction terms between treatment and a priori moderator variables (e.g., acculturative
stress) to examine the possibility that the relationship between a putative moderator and
outcome differ across treatments (Aiken & West, 1991).

In the event of a significant interaction that indicates moderation, investigators will
probe the relationship methods appropriate for multilevel models (Bauer & Curran, 2005).

Mediation analysis will be conducted using a growth-curve framework implemented in an SEM.
Models will be constructed by first fitting growth models for mediators and outcomes and
then fitting mediational growth models. Investigators will follow the same sequence
described above for establishing the best model of longitudinal change for Aim 1. Latent
growth models will be comprised of at least two latent factors; one factor will represent
the initial status, and one or more factors will represent the growth rate of a variable,
where more than one factor will be required in the event of non-linear change (e.g., a
quadratic term). Mediation will be examined following recommendations by MacKinnon (2008)
for assessing mediation in the growth models context. The growth factor of the mediator will
be regressed on the initial status of the mediator, the outcome, and the intervention group.
A significant effect for the intervention group establishes a relation between the
intervention group and the mediator, controlling for baseline levels of the mediator and
outcome. Next, the growth factor will be regressed on the initial status of the mediator,
the outcome, the slope of the mediator, and the intervention group. A significant effect of
the mediator growth factor establishes a relation between change in the mediator and change
in the outcome, controlling for baseline levels of the mediators and outcome.

Data Analysis for Aim 3: Responses to patient and interventionist satisfaction and
assessment of working alliance will be compiled in aggregate form. The frequency of
responses to individual items will be reported for patients and interventionists. Likewise,
scale scores for patients and providers will be reported using means and standard
deviations. Comparison of responses of patients and interventionist will be made using
chisquare in the case of frequency data and t-tests in the case of scale scores.
Organizational readiness will be assessed using a pretest-posttest design. The analysis of
pretest-posttest comparison will employ Analysis of Covariance or ANCOVA. In this
nonrandomized design, the main purpose of ANCOVA is to adjust the posttest means for
differences among groups on the pretest, because such differences are likely to occur. The
purpose of using the pretest scores as a covariate in ANCOVA with a pretest-posttest design
is to (a) reduce the error variance and (b) eliminate systematic bias.

Inclusion Criteria:

- Injury currently being treated at University Medical Center

- Drinking: weekly average of 15 drinks or more or 5 drinks or more on any day in past
year

- Hispanic, Latino, Mexican, or Mexican American

- Speaks Spanish, English or both

Exclusion Criteria:

- Non-injury