Abstract
Problem
Persisting inequities in cardiovascular diseases and diabetes across Europe are largely driven by socio-economic, political, and environmental contexts. Insufficient attention to power dynamics and intersecting social identities can produce knowledge and data gaps, which in turn can lead to interventions that unintentionally reproduce or exacerbate inequities.
Description of the problem
Context refers to structural conditions shaping the distribution of power, resources, and opportunities in society. From a data perspective, this involves being aware of power imbalances within target populations, identifying the social identities and groups which are most exposed to disadvantage, and anticipating differential impacts of both universal and targeted interventions. It also means questioning representativeness, striving for the visibility of hard-to-reach populations, and selecting indicators able to capture life-course trajectories.
Effects
This presentation provides public health researchers, professionals and decision-makers with keys skills for more equitable data generation and evidence-based decision-making. This strengthens skills in identifying hidden barriers, recognising trade‑offs between inclusivity and generalisability, and aligning public health measures and interventions with principles of proportionate universalism. Practical examples are presented for data collection and analysis, including context-socioeconomic indicators and interpretation through life‑course and intersectional lenses. Benefits of combined use of quantitative and qualitative data are discussed.
Lessons
Addressing health inequities requires treating context and data as core components of intervention design, not as technical add‑ons. Continuous critical reflection on power, data and indicator meaning enhances equity‑oriented decision‑making and supports the development of interventions that are more accessible, fair, and responsive to population diversity.