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.

Inclusion criteria

for Primary and Secondary Outcomes:

• Patient diagnosed with a mental health condition in outpatient primary care, prescribed a medication, and given a follow-up date.

Exclusion criteria

for Primary and Secondary Outcomes:

• Patient enrolled in treatment outside of targeted mental health systems analysis and improvement approach (SAIA-MH) facilities.
• Patients not prescribed a medication.
• Patients not given a follow-up date.

Inclusion criteria

for Qualitative Interviews:

• Mental health workers currently working and collaborating on the treatment of outpatient mental health patients in target clinics in Sofala or Manica provinces, Mozambique.
• Mental health workers must be employed by Ministry of Health.
• Mental health managers or directors currently supervising mental health workers who are leading treatment of outpatient mental health patients in target clinics in Sofala or Manica provinces, Mozambique. Must be employed by the Ministry of Health.

Exclusion criteria

for Qualitative Interviews:

• Health worker not involved in outpatient mental healthcare delivery. Health worker not employed by the Ministry of Health.