Search

Background: Healthcare systems in low-resource settings need simple, low-cost interventions to improve services and address gaps in care. Though routine data provide opportunities to guide these efforts, frontline healthcare workers (HCW) are rarely engaged in analyzing them for facility-level decision making. The Systems Analysis and Improvement Approach (SAIA) is an evidence-based, multi-component implementation strategy that engages HCW in use of facility-level data to promote systems-level thinking and quality improvement (QI) efforts within multi-step care cascades. SAIA was originally developed to address HIV care in resource-limited settings, but has since been adapted to a variety of clinical care systems including cervical cancer screening, mental health treatment, and hypertension management, among others; and across a variety of settings in sub-Saharan Africa and the United States. We aimed to extend the growing body of SAIA research by defining the core elements of SAIA using established specification approaches, and thus improve reproducibility, guide future adaptations and lay the groundwork to define its mechanism of action. Methods: Specification of the SAIA strategy was undertaken over 12 months by an expert panel of SAIA-researchers, implementing agents and stakeholders using a three-round, modified nominal group technique approach to match core SAIA components to the Expert Recommendations for Implementing Change (ERIC) list of distinct implementation strategies. Core implementation strategies were then specified according to Proctor’s recommendation for specifying and reporting, followed by synthesis of data on related implementation outcomes linked to the SAIA strategy across projects.Results: Based on this review and clarification of the operational definitions of the components of the SAIA, the four components of SAIA were mapped to 13 ERIC strategies. SAIA strategy meetings encompassed external facilitation, organization of provider implementation meetings, and provision of ongoing consultation. Cascade analysis mapped to three ERIC strategies; facilitating relay of clinical data to providers, use of audit and feedback of routine data with healthcare teams, and modelling and simulation of change. Process mapping tied to local needs assessment, local consensus discussions and assessment of readiness and identification of barriers and facilitators. Finally, continuous quality improvement encompassed tailoring strategies, developing a formal implementation blueprint, cyclical tests of change and purposefully re-examining the implementation process.Conclusions: Specifying the components of SAIA provides improved conceptual clarity to enhance reproducibility for other researchers and practitioners interested in applying the SAIA across novel settings.

Background: The Systems Analysis and Improvement Approach (SAIA) is an evidence-based, multi-component implementation strategy that engages service providers in the use of routinely-available service data to optimize service delivery cascades and promote systems-level thinking. SAIA was originally developed to address bottlenecks in HIV care in low-and middle-income countries, but has since been adapted and applied to a variety of care systems including: cervical cancer screening, mental health treatment, hypertension management, family planning, and community-based naloxone distribution. These projects have been implemented across a variety of settings in sub-Saharan Africa and the United States. Given the diversity of implementation experience, our consortium aimed to define the core elements of SAIA, to improve reproducibility, guide future adaptations, and lay the groundwork to evaluate mechanisms of action. Methods: Specification of the SAIA strategy was undertaken over 12 months by an expert panel of SAIA researchers, implementing agents and stakeholders, using a three-round, modified nominal group technique approach to match core SAIA components to the Expert Recommendations for Implementing Change (ERIC) list of distinct implementation strategies. Core implementation strategies were then specified according to Proctor’s recommendation for specifying and reporting, followed by synthesis of data on related implementation outcomes linked to the SAIA strategy across projects.Results: The four components of the SAIA strategy: (1) SAIA strategy meetings; (2) cascade analysis; (3) process mapping; and (4) continuous quality improvement, mapped to 13 distinct ERIC strategies. The SAIA strategy meetings component mapped to external facilitation, organization of provider implementation meetings, and provision of ongoing consultation. Cascade analysis mapped to facilitating relay of clinical data to providers, use of audit and feedback, and modelling and simulation of change. Process mapping tied to local needs assessment, local consensus discussions, and assessment of readiness and identification of barriers and facilitators. Continuous quality improvement encompassed tailoring strategies, developing a formal implementation blueprint, cyclical tests of change and purposefully re-examining the implementation process.Conclusions: Formally specifying the core components of SAIA provides improved conceptual clarity to enhance reproducibility for other researchers and practitioners interested in applying the SAIA across novel settings. Furthermore, this work provides a structured framework to examine potential mechanisms of SAIA and its component implementation strategies.

National Institutes of Health (NIH) Background: Depression is one of the leading causes of disability in Mozambique; however, few patients with depression are identified in primary care. To our knowledge, there are no validated tools for depression screening in Mozambique. The aim of this study was to validate the Patient Health Questionnaire-9 (PHQ-9) for use in primary care settings in Mozambique. Methods: The PHQ-9 was adapted using a structured multi-phase process led by a team of bilingual experts followed by a review by lay individuals and pilot-testing including cognitive interviews. The final Mozambican PHQ- 9 (PHQ-9-MZ) was applied among 502 individuals randomly selected from antenatal, postpartum, and general outpatient consultations in three Ministry of Health primary healthcare clinics in Sofala Province, Mozambique. The PHQ-9-MZ was evaluated against the MINI 5.0-MZ as a gold standard diagnostic tool. Results: The majority of participants were female (74%), with a mean age of 28. Using the MINI 5.0-MZ, 43 (9%) of the sample tested positive for major depressive disorder. Items of the PHQ-9-MZ showed good discrimination and factor loadings. One latent factor of depression explained 54% of the variance in scores. Questions 3 (sleep) and 5 (appetite) had the lowest item discrimination and factor loadings. The PHQ-9-MZ showed good internal consistency, with a Cronbach’s alpha of 0.84, and an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI: 0.73, 0.89). The PHQ-2-MZ had an AUROC of 0.78 (95% CI: 0.70, 0.85). Using a cut-point of ≥9, the PHQ-9-MZ had a sensitivity of 46.5% and a specificity of 93.5%. Using a cut-point of ≥2, the PHQ-2-MZ had a sensitivity of 74.4% and a specificity of 71.7%. Increasing the cut-point to ≥3, the PHQ-2-MZ has a sensitivity of 32.6% and a specificity of 94.6%. Conclusions: The PHQ-9-MZ and PHQ-2-MZ emerge as two valid alternatives for screening for depression in primary health care settings in Mozambique. Depending on program needs and weighing the value of minimizing false positives and false negatives, the PHQ-9-MZ can be employed with cut-points ranging from ≥8 to ≥11, and the PHQ-2-MZ with cut-points ranging from ≥2 to ≥3. Department of Mental Health, Sofala Provincial Health Directorate, Ministry of Health, Rua Poder Popular n.° 11 – 50. Caixa Postal 583. 4° Andar, Beira, Sofala, Moçambique Faculdade de Medicina, Universidade Eduardo Mondlane, Maputo, Moçambique Health Alliance International, Beira, Mozambique Health Alliance International, Seattle, WA, USA Department of Global Health, University of Washington, Seattle, WA, USA Department of Epidemiology, University of Washington, Seattle, WA, USA Departamento de Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil