Decision Framework for Prioritizing Systematic Reviews Versus Primary/translational Research in Low- and Middle-income Countries.

Abstract

Systematic reviews and meta-analyses (SR/MAs) are widely regarded as the highest level of evidence and play a crucial role in informing clinical practice, public health policy, and guideline development. However, the rapid proliferation of evidence syntheses has raised concerns regarding research redundancy, publication-driven incentives, and the potential diversion of resources away from primary and translational research, particularly in low- and middle-income countries (LMICs). In settings characterized by large and diverse populations, evolving health systems, and substantial unmet health needs, strategic decisions regarding whether to undertake evidence synthesis or generate new evidence are increasingly important. This viewpoint proposes the Research Strategy Decision Pathway, a context-sensitive framework designed to guide researchers, institutions, and funding agencies in determining when SR/MAs are warranted and when primary or translational research should be prioritized. The framework evaluates five domains: (i) contextual specificity of the research question, (ii) availability of recent high-quality systematic reviews or meta-analyses, (iii) methodological robustness and consistency of existing primary studies, (iv) transferability of available evidence to the target LMIC setting, and (v) the potential added value of further evidence synthesis compared with new primary or translational research. A complementary scoring approach is proposed to enhance transparency and reproducibility while allowing contextual judgment to override numerical thresholds when critical limitations exist. The framework integrates concepts from research-prioritization methodologies, evidence-gap mapping, and research-waste reduction initiatives, while addressing a distinct methodological question: whether future scientific investment should focus on evidence synthesis or evidence generation. By promoting methodological proportionality rather than rigid adherence to traditional evidence hierarchies, the framework aims to reduce duplication, strengthen translational capacity, and optimize research investments. Future research should focus on Delphi-based calibration, stakeholder engagement, and empirical validation across diverse LMIC contexts to establish the framework's reliability, applicability, and policy relevance.