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Catching Up on Health Outcomes: The Texas Medication Algorithm Project
Author(s) -
Kashner T. Michael,
Carmody Thomas J.,
Suppes Trisha,
Rush A. John,
Crismon M. Lynn,
Miller Alexander L.,
Toprac Marcia,
Trivedi Madhukar
Publication year - 2003
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.00117
Subject(s) - psychological intervention , depression (economics) , statistic , medicine , major depressive disorder , mental health , intervention (counseling) , psychiatry , statistics , mathematics , cognition , economics , macroeconomics
Objective. To develop a statistic measuring the impact of algorithm‐driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment‐as‐usual controls to “catch up” to early gains of treated patients. Data Sources/Study Setting. Statistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age≥18) with bipolar disorder ( n =267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. Study Design. Study patients were assessed at baseline and three‐month follow‐up for a minimum of one year. Program tracks were assigned by clinic. Data Collection/Extraction Methods. Hierarchical linear modeling was modified to account for declining‐effects. Outcomes were based on 30‐item Inventory for Depression Symptomatology—Clinician Version. Principal Findings. Declining‐effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining‐effects cases. Bipolar patients with severe depressive symptoms in an algorithm‐driven, disease management program reported fewer symptoms after three months, with treatment‐as‐usual controls “catching up” within one year. Conclusions. In addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining‐effect analyses may be applicable to a wide range of treatment and intervention trials.