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Published on: October 6, 2023
National reimbursement databases: Use and limitations for rheumatologic studies
1Institut Pierre-Louis d'Épidémiologie et de Santé Publique, INSERM UMRS 1136, 56, boulevard Vincent-Auriol, 75646 Paris, France; Sorbonne Université, Assistance publique-Hôpitaux de Paris, Rheumatology department, Pitié-Salpêtrière hospital, 47-83, boulevard de l'Hôpital, 75013 Paris, France.
National Reimbursement Databases (NRD) offer valuable insights into rheumatology epidemiology and treatment effectiveness. However, limitations in clinical data hinder comprehensive analysis, necessitating improved data integration.
Area of Science:
- Rheumatology
- Health Informatics
- Public Health
Background:
- National Reimbursement Databases (NRD) are increasingly utilized in medical research.
- Understanding their application and limitations in rheumatology is crucial for effective use.
Purpose of the Study:
- To review and analyze the current knowledge on the use and limitations of NRDs in rheumatology.
- To categorize NRDs based on available clinical data.
Main Methods:
- Categorization of NRDs into three groups based on data content: no clinical details, diagnosis details, or linkable to clinical databases.
- Review of existing literature and database structures.
Main Results:
- NRDs facilitate cohort construction, enabling powerful statistical analysis for epidemiological studies.
- Identified NRD categories include those without clinical details (e.g., French NRD), with diagnosis details (e.g., British NRD), and linkable to clinical databases (e.g., Swedish NRD).
- NRDs aid in understanding disease epidemiology, treatment risk-benefit, and economic impact, informing public health decisions and guidelines.
Conclusions:
- NRDs present significant opportunities for rheumatology research, particularly in epidemiology and health economics.
- Key limitations include the lack of exhaustive clinical and paraclinical data (e.g., disease activity, test results).
- Future efforts should focus on linking NRDs with clinical registers to overcome data deficiencies and enhance research capabilities.
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