Cost-Effectiveness Analysis of a Continuing Care Intervention for Cocaine-Dependent Adults

INTRODUCTION:

The study conducts a cost-effectiveness analysis (CEA) of a continuing care Telephone Monitoring and Counseling (TMC) intervention for adults diagnosed with cocaine dependence. Participants were randomly assigned to a control condition of intensive outpatient treatment only (treatment-as-usual, or TAU; N=108), or to one of two treatment conditions featuring TMC (N=106) and TMC plus incentives (TMC-plus; N=107). Follow-up assessments were conducted over a 2-year period.

METHODS:

Intervention and client costs were collected with the program and client versions of the Drug Abuse Treatment Cost Analysis Program (DATCAP). Effectiveness was measured as the number of days abstinent during follow-up. Secondary analyses consider alternative measures of effectiveness and the reduced societal costs of physical and mental health problems and criminal justice involvement.

RESULTS:

From the societal perspective, TMC dominates both TAU and TMC-plus as a cost-effective and cost-saving intervention. Results varied by substance-using status, however, with the subgroup of participants in TMC-plus that were using drugs at intake and early in treatment having the greatest number of days of abstinence and generating similar savings during follow-up than the TMC subgroup using drugs at intake.

CONCLUSIONS:

Telephone monitoring and counseling appears to be a cost-effective and potentially cost-saving strategy for reducing substance use among chronic substance users. Providing client incentives added to total intervention costs but did not improve overall effectiveness.

Purchase full article at:  http://goo.gl/A5Ehb5

By:  McCollister K1, Yang X2, McKay JR3.

• 1Department Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, United States. Electronic address: kmccolli@miami.edu.
• 2Department Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, United States.
• 3University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, United States  

More at:  https://twitter.com/hiv_insight

And:        http://twitter.com/Prison_Health

Measuring Outcome in the Treatment of Cocaine Dependence

Below:  Patterns of Cocaine Use from Baseline to Month 18 Among Patients Identified as “Continued Users” (N=215) During Months 1 to 4 of Active Treatment. Note: The proportion of patients who used cocaine at each assessment is evaluated on a compositive cocaine use measure that combined information from self-report, urines, and the Addiction Severity Index cocaine use item.

Below:  Patterns of Cocaine Use from Baseline to Month 18 Among Patients Identified as “Slower Improvers” (N=90) During Months 1 to 4 of Active Treatment

Below: Patterns of Cocaine Use from Baseline to Month 18 Among Patients Identified as “Immediate Abstainers” (N=125) During Months 1 to 4 of Active Treatment

Measures of during-treatment reduction in use were moderately correlated with drug and cocaine use measures 12 months, but showed non-significant or small correlations with measures of functioning at 12 months. Highest correlations were evident for abstinence measures (maximum consecutive days abstinence and completely abstinent) during treatment in relation to sustained (3 month) abstinence at 12 months. Latent class analysis of patterns of change over time revealed that most patients initially (months 1 to 4 of treatment) either became abstinent immediately or continued to use every month. Over the couse of follow-up, patients either maintained abstinence or used regularly – intermittent use was less common.

There were generally small associations between various measures of cocaine use and longer-term clinical benefits, other than abstinence was associated with continued abstinence. No one method of measuring outcome of treatment of cocaine dependence appears superior to others.

Read more at:  http://ht.ly/S4Nt6

By: Paul Crits-Christoph,a Robert Gallop,b Mary Beth Connolly Gibbons,a Jaclyn S. Sadicario,a and George Woodya

^b

^b

aDepartment of Psychiatry, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
bApplied Statistics Program, Department of Mathematics, West Chester University, 25 University Avenue, West Chester, PA 19383, USA

aDepartment of Psychiatry, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA

aDepartment of Psychiatry, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA