Introduction: The Need for a Structured Framework
Health inequalities and inequities arise from a web of social determinants and cumulative life-course experiences. Conventional evidence synthesis methods, while valuable, often fall short in capturing the complexity of these factors, the intersections between them, and how they evolve over time. The Kunonga framework emerges as a structured approach to operationalise evidence synthesis for health inequality and inequity research, enabling researchers, policymakers, and practitioners to produce more robust, actionable insights.
What the Kunonga Framework Tries to Solve
The framework addresses several persistent gaps in health equity research. First, it provides a clear map for incorporating intersectionality—how race, gender, socioeconomic status, geography, and other identities intersect to shape health outcomes. Second, it emphasizes life-course perspectives, recognizing that exposures and experiences accumulate and influence health across generations. Third, Kunonga guides analysts in translating diverse types of evidence (quantitative, qualitative, policy documents, and community narratives) into a coherent synthesis that supports decision-making.
Core Components of the Kunonga Framework
The Kunonga framework builds on four interconnected pillars:
- Conceptual Alignment — establishing a shared definition of health inequality and inequity relevant to the research question, and selecting appropriate determinants to study.
- Evidence Mapping — cataloging available literature and data sources, including national statistics, cohort studies, and qualitative insights, to identify gaps and inform synthesis scope.
- Analytical Synthesis Methods — integrating mixed-methods evidence through a transparent, reproducible process that can handle complexity, confounders, and effect modifiers.
- Policy and Practice Translation — translating findings into actionable guidance for reducing inequities, with consideration for feasibility, equity impact, and scalability.
These pillars are designed to be iterative. Researchers revisit definitions, refine evidence maps, and adjust synthesis strategies as new data emerge or as stakeholder needs shift.
Operationalising the Framework: Steps and Methods
1) Define inequality and inequity: Start with a clear, context-specific definition aligned to the policy environment. Identify primary outcomes (e.g., morbidity, mortality, access to care) and subgroups for comparison.
2) Develop an evidence map: Compile datasets, systematic reviews, and qualitative studies. Document the social determinants of health most relevant to the setting (education, income, housing, environment, stigma, etc.).
3) Choose synthesis routes: Decide whether a meta-analysis, a realist review, a scoping synthesis, or a narrative synthesis best captures the complexity. The Kunonga framework supports combining quantitative estimates with qualitative insights to explain mechanisms and contexts.
4) Integrate life-course perspectives: Incorporate longitudinal data where possible and emphasize how early-life exposures influence later health outcomes, enabling a more nuanced interpretation of inequalities.
5) Address intersectionality: Analyze how overlapping identities shape risk and protection, and report results with stratifications that reflect real-world diversity, avoiding over-simplified categories.
6) Assess policy relevance and equity impact: Use predefined criteria to judge the potential impact of findings on policy, including feasibility, acceptability, and equity considerations.
Applications and Case Examples
Applied examples across low-, middle-, and high-income settings illustrate how Kunonga can guide syntheses of obesity, infectious disease, maternal and child health disparities, and access to health services. In each case, the framework helps researchers articulate mechanisms (e.g., access barriers, exposure to risk environments) and prioritize interventions with the greatest potential to reduce inequities.
Strengths, Limitations, and Future Directions
Strengths of the Kunonga framework include its explicit attention to intersectionality, life-course dynamics, and the translation of evidence into policy action. Limitations may include data gaps in certain social determinants or settings, and the methodological challenge of balancing depth with breadth in mixed-method syntheses. Ongoing development aims to enhance standardized protocols, improve tools for uncertainty communication, and foster user-friendly software that supports framework-guided analyses.
Conclusion: Moving from Evidence to Equity
The Kunonga framework offers a structured, adaptable approach to health inequality and inequity evidence synthesis. By foregrounding conceptual clarity, comprehensive evidence mapping, mixed-methods integration, and policy-oriented interpretation, it helps researchers produce actionable insights that support more equitable health outcomes across populations.
