Bringing a health systems modelling approach to complex evaluations: multicountry applications in HIV, TB and malaria

Abstract

IntroductionUnderstanding how to deliver interventions more effectively is a growing emphasis in Global Health. Simultaneously, health system strengthening is a key component to improving delivery. As a result, it is challenging to evaluate programme implementation while reflecting real-world complexity. We present our experience in using a health systems modelling approach as part of a mixed-methods evaluation and describe applications of these models.MethodsWe developed a framework for how health systems translate financial inputs into health outcomes, with in-country and international experts. We collated available data to measure framework indicators and developed models for malaria in Democratic Republic of the Congo (DRC), and tuberculosis in Guatemala and Senegal using Bayesian structural equation modelling. We conducted several postmodelling analyses: measuring efficiency, assessing bottlenecks, understanding mediation, analysing the cascade of care and measuring subnational effectiveness.ResultsThe DRC model indicated a strong relationship between shipment of commodities and utilisation thereof. In Guatemala, the strongest model coefficients were more evenly distributed. Results in Senegal varied most, but pathways related to community care had the strongest relationships. In DRC, we used model results to estimate the end-to-end cost of delivering commodities. In Guatemala, we used model results to identify potential bottlenecks and understand mediation. In Senegal, we used model results to identify potential weak links in the cascade of care, and explore subnationally.ConclusionThis study demonstrates a complementary modelling approach to traditional evaluation methods. Although these models have limitations, they can be applied in a variety of ways to gain greater insight into implementation and functioning of health service delivery.

Bridging the Gap from Molecular Surveillance to Programmatic Decisions for Malaria Control and Elimination

“…126 A nonexhaustive list of these applications includes identification and stratification of malaria risk, [127][128][129] optimization of interventions for national strategic planning, 130 and support for funding requests to the Global Fund. 131,132 Furthermore, routine surveillance data and quantitative risk assessment combined with statistical and model-based analyses have constituted a key component of the National Malaria Elimination Program in China. 133 Despite the existing data and modeling frameworks for decision-making and their uptake by certain NMCPs, only a few small-scale applications have included molecular surveillance data.…”