Introduction: Why a Kunonga framework is needed
Health inequalities and inequities arise from the complex interplay of social determinants across the life course. Traditional evidence synthesis methods often treat risk factors in isolation, missing how education, income, housing, racism, and gender collectively shape health outcomes. The Kunonga framework offers a structured approach to integrate diverse data, concepts, and analytic tools to capture the richness of health inequality evidence. Its aim is to move beyond single-determinant models toward a holistic, equity‑focused synthesis that can inform policy and practice.
Core principles of the Kunonga framework
The framework rests on several guiding principles designed to address limitations in conventional synthesis methods:
- Interdisciplinary integration: combine epidemiology, sociology, economics, and public health to reflect the multifaceted nature of health disparities.
- Life‑course orientation: account for cumulative exposures and critical periods that shape health trajectories.
- Contextualization: incorporate neighbourhood, policy, and cultural contexts that modulate risk and protection.
- Complex systems thinking: model feedback loops and interactions among determinants rather than assuming independent effects.
- Equity emphasis: foreground disadvantaged groups and assess how interventions alter equity in outcomes.
Methodological components
Kunonga integrates several methodological strands to operationalise evidence synthesis for health inequality:
1) Conceptual mapping of determinants
Developing a theory-informed map of social determinants helps clarify how upstream factors like policy environments and downstream outcomes interact. The framework encourages researchers to articulate pathway hypotheses, including mediators and moderators, and to define relevant life-course trajectories.
2) Data integration across sources
Health inequality research benefits from diverse data: population surveys, administrative records, qualitative insights, and policy data. Kunonga prescribes harmonisation strategies, alignment of measures (e.g., education, income, race/ethnicity, deprivation), and transparent handling of missing data to enable valid comparisons across studies and settings.
3) Multi-level analysis and synthesis
Health outcomes are shaped at individual, household, community, and institutional levels. The framework supports a blend of meta-analysis, meta-synthesis, and narrative synthesis, coupled with multi-level modelling to quantify how context modifies effects across levels.
4) Equity-focused metrics
Beyond average effects, Kunonga emphasizes inequality metrics such as concentration indices, slope and relative indices of inequality, and distributional analyses to reveal who benefits or loses from a given intervention or policy.
5) Transparent reporting and replication
Open protocols, preregistration, and explicit documentation of inclusion criteria, data sources, and analytic decisions are central. This ensures reproducibility and enables others to build cumulative evidence on health disparities.
Operationalising the framework in practice
Applying Kunonga requires collaborative teams and careful planning:
- Define the health outcome and the equity question with stakeholder input to ensure relevance to policy and practice.
- Map determinants across the life-course and identify data sources that capture both exposures and outcomes at multiple levels.
- Choose appropriate analytic tools—system dynamics, Bayesian hierarchical models, or robust meta-analytic techniques—that can accommodate complexity and heterogeneity.
- Report by equity strata and use distributional analyses to highlight shifts in health gaps over time.
- Translate findings into actionable policy guidance, emphasizing interventions that reduce disparities without widening them.
Implications for policy and practice
Unlike traditional syntheses, Kunonga supports policymakers in understanding how structural changes—such as housing policy, education access, and social protection—may alter health inequities. By foregrounding intersecting determinants and life-course processes, the framework helps identify leverage points where interventions yield the greatest equity gains and informs targeted implementation strategies in diverse settings.
Conclusion: Advancing equitable health knowledge
Operationalising health inequality evidence synthesis through the Kunonga framework represents a step toward more nuanced, policy-relevant insights. By combining conceptual clarity, data integration, multi-level analysis, and a strong equity focus, Kunonga aims to improve the way researchers generate, interpret, and apply evidence to reduce health disparities for all communities.
