Harnessing Country Experiences for Health Benefit Package Design: Evidence-Informed Deliberative Processes and Experiences From the Joint Learning Network Comment on "Evidence-Informed Deliberative Processes for Health Benefit Package Design - Part II: A Practical Guide".

Amidst competing priorities for allocating finite health resources, using evidence-informed priority setting is a valuable tool for achieving population-level health goals. The paper by Baltussen et al comprehensively reports on the development of practical guidance for evidence-informed deliberative processes (EDPs) which will help with sustainability of programs aimed at universal health coverage (UHC). The authors' experience with the Joint Learning Network for UHC's (JLN) peer-to-peer learning platform on evidence-informed priority setting offers insights on the practical challenges faced by countries in health benefits package (HBP) design, especially to draw in actors to advocate for the priorities and values across the health system. Lessons harvested from JLN countries that have established such advisory committees can provide practical insights for countries in earlier stages of establishing a systematic process for HBP design. Peer-to-peer learning modalities among countries offer viable and effective approaches to institutionalizing EDPs and systematic priority setting.

Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review.

BACKGROUND: Routine health information system(s) (RHIS) facilitate the collection of health data at all levels of the health system allowing estimates of disease prevalence, treatment and preventive intervention coverage, and risk factors to guide disease control strategies. This core health system pillar remains underdeveloped in many low-income and middle-income countries. Efforts to improve RHIS data coverage, quality and timeliness were launched over 10 years ago. METHODS: A systematic review was performed across 12 databases and literature search engines for both peer-reviewed articles and grey literature reports on RHIS interventions. Studies were analysed in three stages: (1) categorisation of RHIS intervention components and processes; (2) comparison of intervention component effectiveness and (3) whether the post-intervention outcome improved above the WHO integrated disease surveillance response framework data quality standard of 80% or above. RESULTS: 5294 references were screened, resulting in 56 studies. Three key performance determinants-technical, organisational and behavioural-were proposed as critical to RHIS strengthening. Seventy-seven per cent [77%] of studies identified addressed all three determinants. The most frequently implemented intervention components were 'providing training' and 'using an electronic health management information systems'. Ninety-three per cent [93%] of pre-post or controlled trial studies showed improvements in one or more data quality outputs, but after applying a standard threshold of >80% post-intervention, this number reduced to 68%. There was an observed benefit of multi-component interventions that either conducted data quality training or that addressed improvement across multiple processes and determinants of RHIS. CONCLUSION: Holistic data quality interventions that address multiple determinants should be continuously practised for strengthening RHIS. Studies with clearly defined and pragmatic outcomes are required for future RHIS improvement interventions. These should be accompanied by qualitative studies and cost analyses to understand which investments are needed to sustain high-quality RHIS in low-income and middle-income countries.