Systems Analysis and Improvement Approach to Optimize the Task-shared Mental Health Treatment Cascade (SAIA-MH)

Due to a shortage of 1.2 million mental health (MH) workers across low- and middle-income countries (LMICs), academic and policy leaders have advocated scaling-up task-sharing to lower-level providers to close the mental health care gap, which exceeds 90% in many LMICs. While task-sharing may increase access to care, limited attention has been paid to quality of care provided by lower-level providers. Task-shared outpatient management of mental health in Mozambique has shown low rates of retention in care (40%), medication adherence (<15%), and proportion of patients achieving function improvement (<5%). Similarly high rates of loss-to-follow-up, poor adherence, and poor patient outcomes have been reported across other LMICs. To our knowledge, there are no evidence-based implementation strategies targeting optimization of the MH treatment cascade in low-resource settings. This is an urgent need for the field of MH care delivery globally.

The MH treatment cascade is a model that outlines the sequential, linked treatment steps that people with mental illness must navigate, from initial diagnosis to symptom/function improvement. Quality problems in one step of a treatment cascade can have non-linear and compounding impacts across the larger complex care system. Implementation strategies focused on only one step in a cascade can potentially contribute to unintended system bottlenecks and quality of care issues. By contrast, the "Systems Analysis and Improvement Approach (SAIA)" is a multicomponent implementation strategy focused on optimizing an entire treatment cascade. SAIA blends facilitation, enhanced local clinical consultation, and the creation of facility-level learning collaboratives with systems-engineering tools in a 5-step approach specifically developed for task-shared providers, which include: (1) cascade analysis to visualize treatment cascade drop-offs and prioritize areas for system improvements; (2) process mapping to identify modifiable facility-level bottlenecks; (3) identification and implementation of modifications to improve system performance; (4) assessment of modification effects on the cascade; and (5) repeated analysis and improvement cycles. A previous cluster RCT established effectiveness of SAIA for HIV treatment cascade improvement (R01HD075057; PI: Sherr). However, no effectiveness data exist on SAIA applied to other complex treatment cascades - such as task-shared MH care. Preliminary data suggest that applying SAIA to MH treatment cascade optimization (SAIA-MH; R21MH113691; PI: Wagenaar) is feasible, acceptable, and can result in clinically-significant treatment cascade improvements; Five months of SAIA-MH implementation resulted in a 1.5-fold increase in medication adherence (aOR: 1.5; CI: 1.2, 1.9) and a 3.7-fold increase in function improvement (aOR: 3.7; CI: 2.5, 5.4). These data suggest that SAIA-MH is a promising strategy for task-shared MH systems improvement globally. Our specific aims are to:

Primary Aim 1: Test the effectiveness of the SAIA-MH strategy using a pragmatic cluster RCT design and assess determinants of implementation success. The investigators will implement SAIA-MH using a 3-year parallel cluster RCT across 8 intervention and 8 attentional control facilities and evaluate effects on mental health function improvement (primary) and retention / medication adherence (secondary). Two years of study implementation will be followed by a 1-year maintenance phase to examine routine fidelity and sustainability. The Consolidated Framework for Implementation Research (CFIR) will be used to assess determinants of implementation success.

Secondary Aim 1: Test causal pathway models to analyze mechanisms of action for effects (or non-effects) of the SAIA-MH implementation strategy. Using 3-years of monthly data on strategy-mechanism linkages, moderators, preconditions, and outcomes for the full 8 intervention and 8 attentional control facilities, the investigators will examine causal pathway effect estimates using longitudinal structural equation modeling. Qualitative CFIR data from Primary Aim 1 will contextualize quantitative path analyses.

Specific Aim 2: Estimate the cost and cost-effectiveness of scaling-up SAIA-MH in Mozambique. The investigators will conduct micro-costing and time-and-motion observation of the SAIA-MH RCT to estimate costs of implementing the intervention. The investigators will construct a Markov model parameterized with cost and outcome data from the SAIA-MH RCT to project budget impact and cost-effectiveness for SAIA-MH scale-up to provincial and national levels.