The Quality Improvement for Depression Collaboration: general analytic strategies for a coordinated study of quality improvement in depression care

Abstract

It is difficult to evaluate the promise of primary care quality-improvement interventions for depression because published studies have evaluated diverse interventions by using different research designs in dissimilar populations. Preplanned meta-analysis provides an alternative to derive more precise and generalizable estimates of intervention effects; however, this approach requires the resolution of analytic challenges resulting from design differences that threaten internal and external validity. This paper describes the four-project Quality Improvement for Depression (QID) collaboration specifically designed for preplanned meta-analysis of intervention effects on outcomes. This paper summarizes the interventions the four projects tested, characterizes commonalities and heterogeneity in the research designs used to evaluate these interventions, and discusses the implications of this heterogeneity for preplanned meta-analysis.

Introduction

Recently, policy makers have given considerable attention to closing the gap in quality of care delivered for many chronic conditions in “best” versus “usual” care settings by funding the development of interventions to implement best practice [1]. Depression is a useful condition to learn more about “closing the gap” because it is prevalent [2], extremely disabling [3], [4], [5], [6], responsive to readily available treatments [7], and poorly managed in many primary care practices where it usually presents [8], [9], [10], [11], [12], [13]. Deriving specific conclusions about how implementing best practice for primary care depression may affect patients in diverse practices is complicated because published estimates have been derived by evaluating distinctive interventions in dissimilar populations using different research designs [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35].

Traditional meta-analysis has become a mainstay for drawing meaningful conclusions about intervention efficacy. Its use is most straightforward when virtually identical interventions are evaluated in large numbers of studies conducted in diverse practice settings by using similar research designs. Unfortunately, these conditions are difficult to realize in the literature examining how to improve primary care treatment for mental health problems. Current studies do not evaluate virtually identical interventions across diverse practice settings; rather, investigators deliberately tailor interventions to the practice setting. In addition, key aspects of the research design differ. As an alternative to relying entirely on qualitative methods to synthesize findings across the existing literature, investigators have begun exploring preplanned meta-analysis [36], [37], [38], [39]. Preplanned meta-analysis recognizes that pooling data across a small number of high-quality studies has the potential to provide a more precise and generalizable estimate of intervention effects than any single study alone if threats to internal and external validity arising from differences across research protocols can be addressed by using appropriate analytic strategies.

The Quality Improvement for Depression (QID) collaboration joins other cooperative studies designed for preplanned meta-analysis [40], [41], [42]. The preplanned meta-analysis QID will undertake is a meta-analysis of patient-level data, pooling data from four projects conducted by different research teams funded by different agencies. The goal of the QID collaboration is to test whether quality-improvement interventions based on the treatment principles elucidated in earlier studies can be implemented with sufficient integrity and intensity to enhance outcomes in the diverse types of community-based practice settings that serve most depressed adults. Prior quality-improvement studies in this area have been conducted primarily in staff model HMOs, testing models in which treatment was assigned [17] or directed in part [15], [16], [23], [31] by the research team. In contrast, QID projects were conducted across a variety of practice organizations by using models in which the research team encouraged high-quality treatment [43] rather than assigned it or directly consulted with patients on it. The specific aims of the QID collaboration are to provide precise estimates about: 1) how quality-improvement interventions affect symptom change and functional status in primary care patients with major depression (intent-to-treat analyses); and 2) how high-quality care affects similar outcomes in the same population (as-treated analyses). We define quality improvement as practice-level strategies designed to promote evidence-based care. Because practice-level quality improvement generally tailors an intervention to the local practice structure, it is important to evaluate the value of these interventions by deriving generalizable estimates of their effect across a wide range of practices using society relevance weights. Society relevance weights (also known as importance weights [44]) increase the external generalizability of the findings by assigning numerical weights to each subject that indicate how representative that subject and his/her primary care setting are to patients seeking primary care across the country across a number of policy-relevant characteristics. Even with society relevance weights, the generalizability of QID findings to American primary care may be somewhat limited because the best practice models QID projects tested may have to be further tailored for successful integration into “similar” practices.

In this paper, we provide a concise history of the QID collaboration; describe research design commonalities; characterize QID intervention commonality and heterogeneity; and analyze implications of the heterogeneity in QID research designs for preplanned meta-analysis of patients. Some heterogeneity across interventions is potentially desirable because it is unlikely that a broad diffusion of quality-improvement interventions will follow one particular model; thus, estimates of quality-improvement effects derived across a diverse set of intervention models may be more realistic for informing policy. Heterogeneity across research designs is less desirable because variation introduces alternative explanations for observed relationships; however, the plausibility of these alternative explanations can potentially be reduced by introducing statistical controls to reduce such threats to internal validity.