Introduction: A framework for health inequality evidence syntheses
Health inequalities and inequities arise from the complex interplay of social determinants and life-course experiences. Traditional evidence synthesis often struggles to capture these dynamics in a way that informs policy and practice. The Kunonga framework emerges as a structured approach to operationalising methods for synthesising evidence on health disparities, with a focus on intersectionality, equity, and cumulative disadvantage. This article outlines the development of Kunonga, its core components, and how it can be applied to produce actionable insights for researchers, policymakers, and practitioners.
What is the Kunonga framework?
Kunonga is designed to bridge methodological gaps in health equity research by integrating concepts from epidemiology, social science, and implementation science. At its core, Kunonga provides a modular toolkit that guides researchers through locating, assessing, and synthesising evidence about health inequalities. Rather than a single method, it is a framework for choosing and adapting synthesis techniques to reflect social determinants, life-course trajectories, and structural factors that shape health outcomes.
Key principles guiding Kunonga
The development of Kunonga is anchored in several guiding principles:
- Equity-centered inquiry: Prioritising outcomes that matter for marginalized groups and ensuring that synthesis methods reveal differential impacts across subpopulations.
- Life-course integration: Incorporating cumulative exposure and critical periods to understand when and how inequities emerge and persist.
- Interdisciplinary methods: Combining qualitative, quantitative, and mixed-methods evidence to capture both numerically measurable disparities and the lived experiences behind them.
- Transparency and reproducibility: Clear documentation of decision rules, inclusion criteria, and synthesis processes to support policy uptake.
- Context sensitivity: Accounting for local, national, and cultural contexts that influence health inequalities.
Core components of the Kunonga framework
Kunonga comprises several interconnected modules that researchers can tailor to their evidence synthesis needs:
- Determinant mapping: A structured approach to identify social determinants (income, education, housing, discrimination, access to care) and how they interact to shape health outcomes.
- Intersectional stratification: Techniques to examine how overlapping identities (gender, race/ethnicity, disability, age) affect exposure and vulnerability.
- Life-course synthesis: Methods to integrate longitudinal data, cohorts, and retrospective reports to trace trajectories of health inequalities.
- Quality appraisal with equity lens: Risk of bias assessments that explicitly consider relevance to marginalized groups and potential equity-related biases in primary studies.
- Evidence translation toolkit: Guidance for producing summaries that are intelligible to policymakers, health system leaders, and community stakeholders, with clear equity targets and implementation considerations.
Methodological approaches within Kunonga
Kunonga supports a spectrum of synthesis methods adapted for equity-focused research, including:
- Systematic reviews with equity tagging: Tagging studies by population subgroup and context to reveal differential effects.
- Realist and context-mechanism-outcome configural analyses: Exploring how context shapes the effectiveness of interventions aimed at reducing inequities.
- Network and pathway synthesis: Mapping causal pathways between social determinants and health outcomes to identify leverage points for policy.
- Narrative syntheses with equity narratives: Integrating qualitative findings to explain how experiences of discrimination or barriers to care influence quantitative estimates.
- Dynamic harm-benefit modeling: Estimating how policy or program changes may shift inequity patterns over time within populations.
Applications and implications for policy
By providing a coherent structure for incorporating equity concerns into evidence syntheses, Kunonga aims to improve the relevance of research for decision-makers. Health system planners can use Kunonga to identify which interventions reduce disparities most effectively, for which groups, and under what conditions. The framework also supports ongoing monitoring, enabling researchers and practitioners to update syntheses as new data on inequality becomes available. In public health, Kunonga can help translate complex, multidimensional evidence into actionable recommendations that advance health equity across the life course.
Conclusion: Advancing health equity through rigorous synthesis
The Kunonga framework represents a purposeful shift in health inequality research—moving from siloed, single-method studies toward integrated, equity-focused syntheses. By embracing determinants, intersectionality, and life-course perspectives within a transparent, adaptable toolkit, Kunonga enables more precise, context-aware conclusions that can drive meaningful action to reduce health inequities.
