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Gerald Cochran, PhD; 1 Craig Field, PhD; 2 Michael Forman, MD; Carlos V.R. Brown, MD 4 University of Pittsburgh; 1 University of Texas, El Paso; 2 Baylor.

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Presentation on theme: "Gerald Cochran, PhD; 1 Craig Field, PhD; 2 Michael Forman, MD; Carlos V.R. Brown, MD 4 University of Pittsburgh; 1 University of Texas, El Paso; 2 Baylor."— Presentation transcript:

1 Gerald Cochran, PhD; 1 Craig Field, PhD; 2 Michael Forman, MD; Carlos V.R. Brown, MD 4 University of Pittsburgh; 1 University of Texas, El Paso; 2 Baylor University Medical Center; 3 University Medical Center at Brackenridge 4 EFFECTS OF BRIEF INTERVENTION ON SUBCLASSES OF INJURED PATIENTS WHO DRINK AT RISK LEVELS

2 Background  Screening and brief intervention for injured patients has been shown to be somewhat consistent in its effectiveness It is not clear from current literature which patients are most responsive  Our recent exploratory research with latent class analysis (LCA) and injured Pts who received SBI 2 separate but similar Level-1 trauma center RCTs We found 5 consistent classes Those who reported the greatest improvements: ○ Alcohol-related “accidents” and injuries to self and others ○ Multiple injury-related risks and consequences of alcohol use ○ Limitations of these studies: only experimental groups were analyzed

3 Current study  Purpose of the current study: Identify if subgroups immerge from both Tx and CG Examine differential intervention effects among subgroups

4 Methods  Secondary analysis of data from a multisite RCT in Texas Two Level-1 trauma centers  Participants Adults with at-risk alcohol (AUDIT-C) use or drinking within 6 hours of injury  Interventions: Brief advice, brief MI, brief MI + follow up phone call  Assessments Baseline, 3-, 6, and 12-months

5 Methods  Variables Measurement model (1) Having driven a motor vehicle after having three or more drinksDichotomous (2) Having taken foolish risks while drinkingDichotomous (3) Having gotten into a physical fight while drinkingDichotomous (4) Having been arrested for driving under the influence of alcoholDichotomous (5) Having had trouble with the law (other than driving while intoxicated) because of drinkingDichotomous (6) Having had an accident while drinking or intoxicatedDichotomous (7) Having been physically hurt, injured, or burned while drinking or intoxicatedDichotomous (8) Having injured someone else while drinking or intoxicatedDichotomous Mixed model outcomes (1) Average standard drinks consumed per weekContinuous (2) Percent days abstinentContinuous (3) Percent days heavy drinkingContinuous (4) Maximum number of drinks consumed on one occasionContinuous Mixed model predictors and controls (1) TimeDummy coded categorical (2) InterventionDummy coded categorical (2) Time X interventionDummy coded categorical Controls: site, baseline individual alcohol use severity (AUDIT-C), drinking at the time of the current injury, previous injury treatment, intention/unintentional current injury, race, gender, age, work status, and education level

6 Analyses  Classify analyze approach Measurement models ○ Latent class analysis, Mplus 6 ○ Increasing number of classes tested Mixed models ○ Classes based on posterior probabilities ○ Piecewise models ○ Person was modeled as the random effect; all other predictors were modeled as fixed effects ○ AR-1 covariance structure for repeated observations

7 Results Table #. Baseline characteristics of study participants (N=553) Variable% Male77.8 Age a 34.9 (12.5) Race White41.4 Black25.1 Hispanic28.4 Employed59.1 Education level ≥High school45.8 Previous injury treatment54.1 Current injury intentional26.0 Baseline alcohol severity and use a Audit C4.9 (3.2) Average drinks per week18.7 (31) Percent days abstinent66% (0.3) Percent days heavy drinking22% (0.27) Max consumed13.1 (10.6) a Mean and SD

8 Results Number of classes and fit indices # ClassesAICABICBLRTEntropy

9 Results  Conditional item probabilities of the five class solution

10 Results Average drinks per week Accidents and drinking/driving class VariablesBSEp(95% CI) BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( ) Multiple risk class BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( )

11 Results Percent days abstinent Accidents and drinking/driving class VariablesBSEp(95% CI) BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( ) Multiple risk class BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( )

12 Results Percent days heavy drinking Accidents and drinking/driving class VariablesBSEp(95% CI) BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( )

13 Results Max consumed Accidents and drinking/driving class VariablesBSEp(95% CI) BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( ) Multiple risk class BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( )

14 Results Max consumed Minimal risk class BMI X Time 3 month ( ) 6 month ( ) 12 month ( ) BMIB X Time 3 month ( ) 6 month ( ) 12 month ( )

15 Discussion  A five class solution best fit the data  Classes characterized by accidents and drinking/driving and multiple risks showed most improvement  These findings have possible implications for targeting service delivery or tailoring interventions Pts who respond most likely need brief MI + booster Drinking and driving and fighting and foolish risk classes responded least and may need additional or other treatments

16 Thank you


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