Network Meta-Analysis and Epidemiology Research: The State of Public Health, Challenges, and Opportunities in European Countries

Background: Public health systems in the European Union (EU) are under increasing pressure due to aging populations, non-communicable diseases (NCDs), health inequalities, and climate-related risks. Despite ongoing policy innovations, there is a lack of comparative evidence on the effectiveness of public health interventions. This study aims to fill this gap by evaluating cross-country outcomes to inform health system reform within the framework of the European Health Union. Methods: The study employed the Confidence in Network Meta-Analysis (CINeMA) framework to evaluate public health strategies across 11 EU countries. Interventions were categorized into five domains: digital health, health equity, preventive public health, climate-resilient strategies, and mental health programs. Key outcomes included unmet medical needs, NCD mortality, healthcare access, mental health service utilization, and climate-related illness, supplemented by hybrid AI text-mining for bias and indirectness detection. Results: Digital health and mental health programs demonstrated consistently high-confidence effects, with significant reductions in unmet needs (–9.0% in Ireland, –8.2% in Germany) and increased service use (+12.0% in Sweden). Preventive and climate-focused strategies showed benefits, such as a –10.5% reduction in emergency room visits in Finland, though with greater variability across studies. Equity-based interventions reduced access disparities (–14.1% in Hungary), but often showed moderate imprecision due to inconsistent implementation. Conclusions: Effective governance that integrates digital health, health equity, and climate resilience is essential for preparing public health systems for future challenges. Many EU countries are expanding preventive services and utilizing open-data tools and evidence-driven policymaking to provide valuable insights for shaping EU health strategies and offer scalable recommendations.