From maps to models: revealing hidden governance in cancer prevention

In Europe, coordinating primary cancer prevention is a multi-level puzzle: EU institutions, national governments, and local actors all play a role. But how do we see who really influences whom and where the money flows? Traditional policy documents often hide the informal ties and shadow networks that shape outcomes. Our most recent study uses Net-Map workshops combined with advanced network analysis to make those relationships visible and quantifiable.

We conducted three participatory Net-Map sessions (Jan–Feb 2024) with a national expert panel to map stakeholders and their ties in authority, influence, and funding. We converted qualitative (hand-drawn) maps into relational datasets and modelled the directed funding network using ERGMs, accounting for governance levels (EU, national, local) and mapped influence/authority ties.

Multi-level Stakeholder networks in primary prevention health policy: authority, financial, and influence relationships. The panel depicts node-and-line network visualizations of the money, authority, and influence multi-level ties among the 128 stakeholders identified by the panel of experts. Networks are organized by governance level (EU, National, Local) with nodes colored by organizational level: EU level (pink), National level (blue), and Local level (orange). Edge characteristics indicate relationship direction and scope: solid arrows represent cross-level ties, while dotted lines show within-level connections. Edge sources are differentiated by color (EU=pink, National=blue, Local=orange) and edge span is indicated by line type (within level=dotted, cross level=solid arrows). (a) Authority network showing formal hierarchical relationships and regulatory oversight patterns with 150 directed ties. (b) Money network illustrating financial flows, including funding and resource transfers, with 836 directed ties. (c) Influence network depicting informal power relationships and policy influence pathways with 1,012 directed ties. The hierarchical layout reveals the multi-level governance structure in health policy networks, with varying connectivity patterns across the three relationship types.

The network covers 128 organizations and 836 directed funding ties (density ≈ 5 %). 71.4% of funding ties are cross-level (e.g. EU → national/local). In the models: Influence ties are by far the strongest predictor of funding; Formal authority has a weaker but positive effect; Same-level funding ties are much less likely. These patterns suggest that informal influence networks often align more closely with actual resource flows than formal hierarchies do, and that cross-level (vertical) funding is far more common than horizontal exchanges.

This work is primarily methodological, not a definitive portrait of Romanian or European health systems. We use a single, embedded expert panel and a cross-sectional snapshot to illustrate how one might do an end-to-end Net-Map + ERGM workflow in a governance context. We acknowledge these limitations: structural model fit, potential panel bias, cross-sectional constraints, and frame our findings as hypothesis-generating, not conclusive.

We produce a workflow for researchers interested in combining participatory mapping with statistical causal inference; a diagnostic tool for governance and policy design, especially in decentralized systems; a way to highlight where coordination may fall short or where investments should be redirected.

Further details on https://www.researchsquare.com/article/rs-7837431/v1