Generate evidence to quantify opportunity and justify access
Amongst the real world data sources in Europe, the French Healthcare database SNDS is the largest and potentially the most comprehensive healthcare data resource, covering ~65 million lives, more than 99% of the French population and about 10% of Europe.
Drawing on decades of experience in real-world studies and renowned expertise in SNDS, we offer solutions for every need: from niche needs identification, justifying access, differentiating value, quantifying an opportunity to demonstrating benefit/risk.
- Quantify Opportunity: Unmet needs, treatment patterns, burden of illness, market sizing
- Justify Access: Cost minimization, cost offsets, cost effectiveness and budget impact analysis
- Measure Performance: Drug utilization studies, relative effectiveness, outcomes performance, PAES
- Ensure Safety: Benefit/risk assessment, risk management, signal detection, PASS
Quantify outcomes with patient and population-based research
Certara’s research has been instrumental in demonstrating the benefit that products and health technologies provide to patients. Certara offers real world data solutions for every need and SNDS represents a substantial part of it. Our SNDS studies are trusted by leading innovators to identify treatment patterns and patient outcomes, unmet needs among niche patient groups to target drugs, as well as burden of illness and resource utilization in the real world.
Unmatched quality in data and analysis
Certara experts pioneered the use of electronic health care databases (EHCD) and electronic Medical Records (EMR) for conducting pharmaco-epidemiology studies. Our unique services link observational studies to advanced modeling in access data platforms built for bridging-to-real life studies.
Many of the typical challenges associated with conducting research using claims data can be addressed with appropriate study design and analysis using SNDS. The SNDS coverage (almost all people living in France), its universal nature and its size allow to minimize biases, long follow-up time (~10 years) and make it suitable for studying small patient populations, which is helpful when doing research on rare diseases and drug-related adverse events.