Here’s something most pharma launch teams won’t admit: more than half of all product launches fail to meet expectations. In competitive therapeutic areas, that failure rate climbs to 60%.
The reason? Most teams are still launching products based on clinical trial extrapolations and market assumptions—not real-world evidence of how patients actually navigate the healthcare system, what treatments they’re prescribed, or where the genuine gaps in care exist.
In Continental Europe’s highly regulated pharmaceutical markets, this approach is particularly risky. With HTA bodies demanding robust real-world evidence and reimbursement authorities requiring proof of added therapeutic benefit, launching without aggregated anonymized health insurance claims data is like navigating unfamiliar territory blindfolded.
The good news? Access to Pan-European statutory health insurance claims datasets—covering millions of lives across multiple healthcare systems—has fundamentally changed how sophisticated pharma and MedTech companies approach launch planning. And the shift from assumption-based to evidence-based strategies is saving millions in missteps while dramatically improving launch outcomes.
Why Traditional Launch Planning Falls Short in Continental Europe
Clinical trials are brilliant at demonstrating efficacy under controlled conditions. But they’re terrible at predicting real-world market dynamics.
Think about it: Phase III trials typically enroll highly selected patient populations, exclude common comorbidities, and follow strict protocols that don’t reflect actual prescribing behavior. When your launch forecast is built primarily on these extrapolations, you’re essentially betting your market entry on best-case scenarios.
Continental European markets compound these challenges. Unlike more flexible reimbursement systems, many European countries require manufacturers to demonstrate added therapeutic benefit through formal health technology assessments within the first year of launch. Miss that window or fail to provide convincing real-world evidence to national HTA bodies, and your pricing negotiations suffer for years.
This is where aggregated insurance claims datasets become absolutely critical. As Flynn et al. demonstrated in their 2022 analysis of EMA marketing authorization applications, real-world evidence submissions increased significantly between 2018-2019, reflecting regulators’ growing acceptance of claims-based insights to complement clinical data.
But here’s what many teams still miss: these datasets aren’t just for regulatory submissions. They’re strategic intelligence that should inform every aspect of your launch planning—from patient population sizing to competitive positioning to HCP targeting.
The Strategic Value of Pan-European Health Insurance Claims Data
Continental Europe’s diverse statutory health insurance systems create something unique: comprehensive, longitudinal patient journey data at population scale.
Unlike fragmented healthcare systems where patients move between disconnected providers and payers, European statutory insurance systems capture the entire patient experience—from initial diagnosis (ICD-10 codes) through procedures, prescriptions (ATC classification), and healthcare utilization across ambulatory and inpatient settings.
What makes this particularly powerful for launch planning is the granularity and breadth. You’re not looking at a convenience sample or registry subset… you’re analyzing treatment patterns for millions of insured lives, with regional-level insights that reveal variations in prescribing, access barriers, and disease prevalence across different healthcare systems.
Makady et al.’s comparative study of HTA agencies highlighted how European countries take distinctly rigorous approaches to real-world evidence in health technology assessment. This means the same aggregated claims data you use for launch strategy can later support your HTA dossiers—assuming you’ve structured your analysis appropriately from the start.
From Data Points to Patient Journeys
The longitudinal nature of claims data reveals something clinical trials can’t: how patients actually move through the healthcare system over time.
You see treatment sequences. Switches between therapies. Adherence patterns. Hospitalization rates. Time to treatment intensification. Gap periods where patients go untreated. All of this provides a realistic baseline for forecasting how your product might fit into existing treatment pathways.
For instance, in preparing for a diabetes therapy launch, claims data would show you the typical journey from metformin monotherapy to dual therapy to basal insulin—including how long patients spend at each stage, which combinations are most common, and where drop-offs occur. This isn’t theoretical… it’s what’s happening right now in medical practices and hospitals across Continental Europe.
Precise Patient Population Sizing and Disease Prevalence Assessment
The first critical application for launch planning is getting the market size right.
Standard industry approach: take clinical trial prevalence estimates, apply them to census data, make optimistic assumptions about diagnosis rates and treatment-seeking behavior, and hope you’re in the ballpark.
Evidence-based approach using aggregated claims: analyze actual diagnosed patient populations with the specific indication you’re targeting, broken down by severity, comorbidities, age bands, geographic distribution, and current treatment status.
The difference in accuracy is frankly staggering. Dang’s 2023 primer on real-world evidence emphasizes how claims databases have become the gold standard for post-marketing epidemiology specifically because they capture diagnosis and treatment patterns as they occur—not as epidemiologists model them.
In Continental Europe, this means you can answer questions like:
- How many patients are currently diagnosed with your target condition across different regions and healthcare systems?
- What’s the actual incidence rate based on new diagnosis codes appearing in the data?
- Which patient segments are undertreated or using outdated therapies?
- What percentage have the specific comorbidity profile your clinical program studied?
- Where are concentrations of high-prevalence regions or centers of excellence?
These aren’t hypotheticals. Hoffmann et al.’s foundational review from 2009 on health insurance medication claims established these datasets as reliable sources for prevalence estimation and drug utilization studies—and their application has only expanded as data access and analytical capabilities have improved.
Moving Beyond Simple Prevalence to Addressable Populations
But prevalence alone doesn’t tell you your true market opportunity. You need to understand the addressable patient population based on real-world constraints.
Claims data lets you filter patient cohorts by treatment history, contraindications (via diagnosis codes), specialist access, and payer coverage patterns. This transforms a rough epidemiological estimate into a precise forecast of patients who are actually eligible for and likely to receive your therapy in European markets.
For specialty products requiring specific diagnostic pathways or biomarker testing, you can even track diagnostic uptake rates to model realistic patient identification. This is particularly valuable in oncology, rare diseases, or precision medicine launches where the diagnostic journey is complex.
Identifying Unmet Needs and Treatment Gaps
The second major value driver: understanding where current therapies fall short and where your product can genuinely add value.
Standard launch messaging often defaults to clinical trial endpoints: “Our drug reduced HbA1c by X%.” But payers and prescribers in Continental Europe want to know: compared to what patients are actually receiving today, what’s the real-world benefit?
Aggregated claims data reveals treatment gaps in several dimensions:
Treatment persistence and adherence. If you see high discontinuation rates for existing first-line therapies within six months, that signals an opportunity for improved tolerability or convenience. Claims track prescription fills over time, so you can calculate persistence curves and identify where patients abandon treatment.
Treatment intensification delays. When patients remain on failing regimens for extended periods before switching or adding therapies, that indicates clinical inertia—and potentially an opportunity for earlier intervention if your product can demonstrate superior outcomes.
Undertreated populations. Claims may reveal patient segments with documented diagnoses who never receive guideline-recommended therapy. This could be due to access barriers, contraindications, tolerability concerns, or simply lack of available options. Each represents a different type of unmet need your launch strategy should address.
Wang et al.’s 2022 study in Nature Communications demonstrated how reproducible real-world evidence from claims databases can inform regulatory and coverage decisions—precisely because these patterns of treatment gaps and suboptimal outcomes are observable at scale and validatable across different datasets.
Burden of Illness Quantification
For HTA submissions and payer negotiations, you’ll need evidence of disease burden. Claims data provides exactly that: healthcare utilization, hospitalization rates, emergency department visits, outpatient encounters, and associated costs—all stratified by patient characteristics and treatment status.
Ohlmeier et al.’s 2018 systematic review examined health economic evaluations based on European routine data, confirming that claims are widely used for cost and outcomes analyses relevant to assessing market potential and demonstrating value to payers.
This becomes powerful when you can show that patients using existing standard of care still experience high burden—frequent hospitalizations, multiple specialist visits, progression to complications—and your therapy is designed to address exactly those outcomes. That’s a value story grounded in European real-world evidence, not just trial efficacy.
Competitive Mapping and Uptake Forecasting
Understanding the competitive landscape goes far beyond market share reports. You need to know how prescribers actually behave, how quickly analogous products achieved adoption, and what barriers slowed or accelerated uptake.
Aggregated insurance claims data enables sophisticated competitive intelligence:
Market share dynamics over time. Track how competing products gained (or lost) share at launch and through the lifecycle. Identify which specialties adopted quickly versus slowly. Understand seasonal patterns, regional variations, and the impact of market access milestones like additional indications or reimbursement changes.
Prescriber segmentation. Not all physicians prescribe the same way. Claims data reveals high-volume prescribers, early adopters who quickly trial new therapies, and conservative prescribers who stick with established options. This segmentation is absolutely critical for targeting your sales force and medical affairs activities.
Line of therapy positioning. Where do competing products typically appear in treatment sequences? Are they prescribed as first-line options, used after specific failures, or reserved for particular patient types? Understanding these patterns helps you position your product realistically and identify opportunities for differentiation.
Combination therapy trends. In many therapeutic areas, real-world treatment involves combinations not studied in trials. Claims show which drugs are prescribed together, helping you anticipate potential combination use of your product and identify safety monitoring needs or opportunities for combination development.
Gensorowsky et al.’s 2022 analysis of market access for digital health applications in Europe highlighted the role of claims-based real-world evidence in navigating reimbursement frameworks—principles that apply equally to traditional pharmaceuticals and devices.
Analog-Based Uptake Modeling
For truly evidence-based forecasting, identify analogous launches—similar mechanism of action, indication, positioning, pricing tier—and model your uptake curve based on their historical performance captured in claims data.
This approach accounts for market-specific factors that generic S-curves miss: HTA timing, regional rollout patterns, KOL influence, payer negotiation timelines. You’re forecasting based on how similar products actually performed in European markets, not theoretical diffusion models.
Dagenais et al.’s 2022 review in Clinical Pharmacology & Therapeutics demonstrated how claims data supports drug development strategy and clinical trial design by revealing patient journeys, epidemiology patterns, and launch dynamics that inform realistic planning.
Navigating European Market Access with Evidence-Based Strategies
Continental Europe’s market access pathways are unique and demanding. National HTA processes require submission of comprehensive dossiers demonstrating comparative effectiveness and cost-effectiveness, followed by assessment of added therapeutic benefit and subsequent pricing negotiations with national health authorities.
Real-world evidence from aggregated claims plays an increasingly important role throughout this process.
For HTA dossiers: While clinical trial data remains the primary evidence base, real-world data can supplement by demonstrating disease burden, describing treatment patterns in the appropriate comparator arm, or providing safety and effectiveness data in broader populations than trial eligibility allowed.
Ruof et al.’s 2022 analysis of benefit assessments from an oncological perspective noted the continuing expansion and new horizons for real-world evidence in European HTA processes—particularly as assessment bodies have become more receptive to well-designed RWE studies that complement randomized controlled trials.
For pricing negotiations: Demonstrating your product’s value often requires showing that it addresses unmet needs not captured in simple efficacy measures. Claims-based evidence of treatment gaps, burden of illness, and real-world outcomes strengthens your negotiating position by grounding value arguments in country-specific data.
For post-launch evidence generation: After reimbursement is established, ongoing claims analysis tracks real-world utilization, safety signals in broader populations, and effectiveness in routine practice—all of which inform label expansions, additional indication development, or re-negotiations.
Regional Variations Matter
Continental Europe isn’t a monolithic market. Prescribing patterns, disease prevalence, access to specialists, and even payer policies can vary significantly across regions and healthcare systems.
Claims data with regional granularity reveals these variations, enabling you to tailor launch strategies accordingly. Perhaps Nordic countries show different prescribing patterns than Southern European regions. Maybe rural areas have lower specialist access, suggesting different HCP targeting priorities. Understanding these nuances prevents one-size-fits-all strategies that miss regional opportunities.
Swart et al.’s chapter in the Springer Health Services Research handbook describes how statutory health insurance claims enable utilization research across different healthcare settings and regions—critical for understanding market heterogeneity.
HCP Targeting and Commercial Strategy Optimization
Even the best product fails if it doesn’t reach the right prescribers.
Traditional physician targeting relies on prescription data from panel sources, which provide volume estimates but limited insight into prescriber behavior, patient mix, or treatment decision patterns. Aggregated claims data offers a more comprehensive view.
High-value prescriber identification. Beyond simple volume, claims reveal which physicians treat your target patient population, have experience with related therapies, and demonstrate prescribing patterns aligned with your product profile. These are your priority targets for early engagement.
Specialty and care setting analysis. Understand which specialties diagnose, initiate therapy, and manage long-term treatment. In Continental Europe, the division between ambulatory and hospital-based care creates distinct channels. Claims show which settings drive volume for your indication, informing channel strategy.
Geographic prioritization. Rather than evenly distributing sales resources, concentrate efforts in regions with high disease prevalence, strong prescriber networks, and favorable treatment patterns. Claims data enables this precision targeting.
Key opinion leader mapping. Identify physicians with large, relevant patient panels who adopted similar products early. These are likely KOL candidates for advisory boards, speaker programs, or early access programs.
Jaksa et al.’s 2019 framework argued that integrated real-world evidence strategies—including claims-based insights for market access and launch efficiency—can save the pharmaceutical industry up to $1 billion annually by improving decision-making and reducing inefficiencies.
Addressing Common Launch Planning Pain Points
The pharmaceutical industry has a launch problem. Despite investing billions in development, roughly 56% of launches fail to meet pre-launch expectations, with that figure climbing to 60% in competitive therapeutic areas.
Aggregated patient data directly addresses the root causes:
Inaccurate forecasting. When only 50% of launches meet analyst forecasts, it’s usually because market size estimates were wrong, uptake assumptions were too optimistic, or competitive dynamics weren’t understood. Claims-based modeling grounds forecasts in actual patient volumes, real prescriber behavior, and validated analog performance.
Poor product differentiation. Generic messaging about efficacy doesn’t cut through. Claims data reveals the specific gaps in current treatment—high discontinuation, poor persistence, inadequate symptom control, excessive burden—that your product addresses. That’s differentiation based on unmet needs, not just MOA novelty.
Misaligned targeting. Spreading resources too thin across physicians who won’t prescribe your product wastes time and money. Data-driven segmentation focuses efforts on high-potential HCPs and institutions where your product fits established practice patterns.
Delayed decision-making. Waiting months for custom analyses while competitors move forward costs opportunities. Modern aggregated claims datasets provide analysis-ready access, enabling rapid strategic pivots based on emerging market intelligence.
Lack of payer evidence. European payers demand proof of real-world value, not just trial efficacy. Claims data provides exactly that evidence base for negotiations and HTA submissions.
Schmitt et al.’s description of European pharmacoepidemiological research databases in the 2021 Springer handbook details how statutory health insurance claims support drug utilization, safety monitoring, and launch-related epidemiology—precisely the applications pharma teams need for successful European market entry.
Navigating the Data Landscape: What to Look For
The challenge many pharmaceutical companies face isn’t recognizing that claims data matters—it’s determining which combination of capabilities, coverage, and analytical sophistication aligns with their strategic priorities.
The claims data landscape is remarkably complex, with dozens of providers offering overlapping but distinct capabilities. Understanding the key dimensions of variation helps frame your evaluation:
Coverage and Geographic Scope
Data coverage varies dramatically across providers. Some maintain comprehensive longitudinal records covering hundreds of millions of patient lives across multiple European countries and payer types. Others focus on specific regions or healthcare systems with greater depth and granularity.
Consider your needs: Are you launching pan-European or country-specific? Do you need broad population coverage for epidemiology, or deep therapeutic-specific datasets for niche indications? The answer determines which coverage profile matters most.
Data Recency and Update Frequency
Timeliness matters differently depending on your use case. Real-time or near-real-time data feeds enable competitive monitoring and early signal detection for post-launch tracking. Fully adjudicated claims—processed months after service delivery—provide greater accuracy for historical analyses and forecasting models.
Most launch planning applications benefit from adjudicated data covering 2-5 years of historical treatment patterns. Post-launch surveillance and competitive intelligence benefit from more recent, frequently updated datasets.
Analytical Capabilities and Tools
The gap between raw claims data and actionable intelligence is substantial. Provider capabilities range from traditional query-based systems requiring significant data engineering to AI-powered platforms offering real-time pattern recognition, predictive analytics, and pre-built therapeutic area modules.
Some organizations have strong internal analytics capabilities and prefer direct data access with custom analysis. Others need full-service analytics support with domain expertise in pharmaceutical commercialization. Understanding where you fall on this spectrum is critical.
Data Integration and Enrichment
Pure claims data captures billing and reimbursement. But some providers enhance this foundation by linking to complementary data sources: laboratory results, specialty pharmacy data, provider characteristics, prescription abandonment, patient socioeconomics, and disease registries.
This vertical integration creates analytical possibilities that claims alone cannot support—particularly for understanding clinical outcomes, diagnostic pathways, and factors driving treatment decisions.
Compliance and Data Governance
GDPR compliance is non-negotiable in Continental Europe, but providers approach anonymization, data security, and audit trails differently. Understanding their specific methodologies, certifications, and track records matters for risk management—especially when using RWE for regulatory submissions.
Some providers offer synthetic data capabilities that preserve statistical properties of real claims while eliminating privacy concerns entirely, enabling broader use cases for modeling and scenario analysis.
Cost Models and Licensing Flexibility
Pricing structures vary widely: per-query fees, annual licenses, therapeutic area subscriptions, project-based pricing, or hybrid models. Total cost of ownership includes not just licensing but also internal resources for data engineering, analysis, and interpretation.
Understanding which model aligns with your usage patterns and budget constraints requires careful evaluation of your anticipated analytical needs across the product lifecycle.
Best Practices for Leveraging Claims Data in Launch Planning
Having access to aggregated insurance claims data is one thing. Using it effectively is another.
Here’s what separates teams that extract maximum value from those that treat it as just another data source:
Start early. Don’t wait until six months before launch to analyze the market. Begin examining claims data during Phase III to validate assumptions, refine target product profiles, and prepare evidence generation strategies. The insights inform protocol amendments, endpoint selection, and even which patient populations to prioritize in trials.
Define requirements before evaluating options. Document your coverage needs (geographies, therapeutic areas, patient populations), analytical capabilities (self-service vs. full-service), integration requirements (API access, data formats), compliance constraints, and budget parameters. This clarity prevents costly mismatches between what you need and what you license.
Integrate across functions. Claims data benefits aren’t limited to commercial teams. Market access needs burden of illness data for payer discussions. Medical affairs needs real-world treatment patterns to support scientific messaging. Clinical development needs epidemiology data for trial design. R&D needs unmet needs assessment for pipeline prioritization. Break down silos and ensure insights flow across stakeholder groups.
Validate assumptions continuously. Markets evolve. New competitors launch. Guidelines change. Payer policies shift. Don’t rely on a single snapshot from 18 months ago. Establish processes for ongoing monitoring and rapid updates to forecasts and strategies as new claims data becomes available.
Combine with complementary data sources. Claims data is powerful but not complete. It captures what was billed, not clinical nuances like disease severity, biomarker status, or patient-reported outcomes. Integrate with electronic health records, registries, or primary research where gaps exist—but let claims provide the population-scale foundation.
Leverage expert guidance for data strategy. The gap between understanding what claims data can do and identifying which specific combination of sources, analytical tools, and domain expertise matches your needs is substantial. Navigating this landscape requires expertise in both pharmaceutical commercialization and the evolving real-world evidence ecosystem. Expert advisors who understand both your business objectives and the data landscape can accelerate your path to value while avoiding common pitfalls that waste time and resources.
Respect privacy and compliance. All analysis must use properly anonymized, GDPR-compliant datasets. In Continental Europe, this means data processed under strict data protection regulations ensuring patient privacy. Work with reputable data providers who demonstrate rigorous anonymization processes and regular audits.
Focus on actionable insights, not data dumps. Don’t get lost in endless analyses. Define clear decision points—market size estimate, target prescriber profiles, uptake assumptions—and focus analytical efforts on answering those specific questions with appropriate confidence intervals and validation.
Pilot before scaling. Start with a focused project to validate that your chosen data approach answers your questions before committing to large-scale multi-year licensing agreements. A 3-6 month pilot in a specific therapeutic area or geography provides proof of value and reveals any gaps between expectations and reality.
The ROI of Evidence-Based Launch Planning
Investing in comprehensive aggregated claims data analysis isn’t cheap. But the alternative—launching blind—is far more expensive.
Consider the costs of launch underperformance: overbuilt sales forces targeting the wrong physicians, manufacturing capacity mismatched to actual demand, missed reimbursement negotiations due to weak evidence, delayed profitability by 1-2 years.
Now consider what evidence-based planning enables: right-sized forecasts that optimize resource deployment, targeted HCP engagement that maximizes ROI on promotional spend, strong payer evidence that accelerates reimbursement, realistic market access strategies that avoid expensive late-stage surprises.
The math is compelling. Improving forecast accuracy by even 10-15 percentage points—entirely achievable with claims-based modeling—can save tens of millions in avoided misinvestments for a mid-sized launch. Accelerating time to adequate reimbursement by three months adds quarters of peak sales to the patent life.
For companies entering European markets for the first time, the value is even higher. You’re competing against established players with deep local knowledge. Claims data levels that playing field by providing the same market intelligence they’ve accumulated over years of presence.
Looking Ahead: The Future of RWD in European Pharma Launches
The trajectory is clear: real-world evidence from aggregated patient data will become table stakes for successful pharmaceutical launches in Continental Europe.
Several trends are accelerating this shift:
Regulatory expectations are rising. EMA’s increasing acceptance of RWE for regulatory decisions, documented in the Flynn analysis, signals that health authorities expect manufacturers to demonstrate real-world performance, not just controlled trial efficacy. National HTA bodies across Europe are following similar trends for benefit assessments.
Payers are demanding value proof. With healthcare budgets strained and innovative therapies increasingly expensive, payers want evidence that new products deliver value in routine practice. Claims-based outcomes data provides exactly that proof.
Data access is improving. Europe’s expanding data infrastructure—from pharmacoepidemiological research databases to commercial data providers—makes comprehensive claims data more accessible than ever. Analysis that once required six months of custom work can now be delivered in weeks with modern platforms.
Advanced analytics are enabling new insights. Machine learning models can identify patient subpopulations, predict treatment response patterns, and optimize targeting strategies from claims data in ways that weren’t possible five years ago. These capabilities will only expand.
Synthetic data is emerging. For scenarios where privacy constraints limit historical claims access, synthetic datasets generated from real-world patterns enable modeling and planning without compromising GDPR compliance. This expands the use cases for evidence-based launch planning.
Companies that embrace these capabilities now will have sustained competitive advantages as evidence-based decision-making becomes industry standard.
Your Next Steps: Moving from Assumptions to Evidence
If you’re planning a pharmaceutical or medical device launch in Continental Europe, the question isn’t whether to use aggregated insurance claims data—it’s when to start and which applications to prioritize.
Begin by identifying your highest-risk assumptions: What market size estimate has the widest confidence interval? Which competitive positioning claims lack real-world validation? Which patient segments are you least certain about? These are where claims data delivers immediate value.
Next, evaluate your current capabilities. Do you have internal expertise in real-world evidence strategy? Can you navigate the complex landscape of data providers, analytical platforms, and domain-specific requirements? What’s your timeline for integrating insights into launch planning processes?
Understanding which combination of data coverage, analytical sophistication, and domain expertise aligns with your specific strategic priorities requires careful evaluation. Most pharmaceutical companies lack the internal market knowledge to assess dozens of overlapping provider capabilities against their nuanced requirements.
Then prioritize applications based on decision timelines. If you’re 18 months from launch, start with patient population sizing and competitive landscape mapping. If you’re 6 months out, focus on HCP targeting and uptake forecasting refinement. If you’re post-launch, emphasize performance tracking and payer evidence generation.
The pharmaceutical industry’s launch success rate won’t improve until teams replace assumptions with evidence. Aggregated anonymized patient data from European health insurance claims provides that evidence foundation—comprehensive, validated, and actionable.
The companies that master evidence-based launch planning today will set the standard for pharmaceutical commercialization through the rest of this decade. The question is whether you’ll lead that transformation or scramble to catch up.
Ready to Transform Your Launch Strategy?
Understanding what types of claims data exist and what questions they can answer is the first step. The second step—matching your specific requirements to the right combination of data sources, analytical capabilities, and domain expertise—requires guidance from professionals who understand both the pharmaceutical industry and the evolving real-world evidence landscape.
The challenge isn’t finding data providers—it’s determining which capabilities align with your strategic priorities and how to extract maximum value from your investment. Expert advisors who know both pharmaceutical commercialization and the data ecosystem can accelerate your path to actionable intelligence while avoiding costly mismatches.
Learn more about strategic approaches to real-world evidence and discover how to navigate the complex data landscape for launch excellence.
Contact us to discuss your specific launch planning challenges and explore evidence-based strategies that transform assumptions into competitive advantages.
Sources & References
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- Swart E, et al. Health Care Utilization Research using Secondary Data. In: Janssen C, et al. (eds) Health Services Research. Springer; 2014. Springer
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