The French health insurance data warehouse named SNDS is one of the largest medico-administrative in the world allowing for powerful pharmacoepidemiological studies, based on real-life data collected prospectively. In addition to the absolute necessity of a strong pharmacological rationale, recommendations have been thought to improve the quality of pharmacoepidemiological studies. These guidelines emphasize the importance of an accurate definition of the study population, outcome and exposure, especially for studies performed on medico-administrative databases. Compliance with certain guidelines, particularly those concerning the identification of a specific population or an outcome and the definition of risk periods or exposure periods, may be difficult when performing studies on the SNDS because of its structure and the nature of the data recorded. The objective of this article is to provide advice for the conduct of pharmacoepidemiological studies according to the recommendationswhen using the SNDS, given its specificities. The performing of reliable studies from this rich but complex data warehouse requires the expertise of researchers with deep knowledge both in the SNDS and in pharmacological reasoning.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.therap.2023.01.010DOI Listing

Publication Analysis

Top Keywords

pharmacoepidemiological studies
16
data warehouse
12
french health
8
health insurance
8
insurance data
8
population outcome
8
studies
7
data
5
snds
5
[performing pharmacoepidemiological
4

Similar Publications

Sulfonylureas (SU) are commonly prescribed as oral hypoglycemic agents for the management of diabetes mellitus (DM). We postulated that SU possess antimicrobial properties due to their structural resemblance to the antimicrobial agent sulfamethoxazole. Using data from Taiwan's National Health Insurance Research Database, we enrolled patients diagnosed with DM between 2000 and 2013 and followed them for a three-year period.

View Article and Find Full Text PDF

This study analyzed the association of romosozumab, a human monoclonal antibody with bone-forming and bone resorption-inhibiting effects, and bisphosphonates with the development of cardiovascular disease among patients with osteoporosis. A new-user design was employed to address selection bias, and instrumental variable analysis was used to address confounding by indication. Japanese patients aged ≥40 years, diagnosed with osteoporosis or experienced a fragility fracture, were admitted to medical facilities covered by a commercial administrative claims database, and newly prescribed romosozumab or bisphosphonates after the commercialization of romosozumab in Japan (March 4, 2019) were included based on verification of a 180-day washout period.

View Article and Find Full Text PDF

A compilation of factors over the past decade-including the availability of increasingly large and rich healthcare datasets, advanced technologies to extract unstructured information from health records and digital sources, advancement of principled study design and analytic methods to emulate clinical trials, and frameworks to support transparent study conduct-has ushered in a new era of real-world evidence (RWE). This review article describes the evolution of the RWE era, including pharmacoepidemiologic methods designed to support causal inferences regarding treatment effects, the role of regulators and other health authorities in establishing distributed real-world data networks enabling analytics at scale, and the many global guidance documents on principled methods of producing RWE. This article also highlights the growing opportunity for RWE to support decision making by regulators, health technology assessment groups, clinicians, patients, and other stakeholders and provides examples of influential RWE studies.

View Article and Find Full Text PDF

Introduction: In Germany, there has been no population-level pharmacoepidemiological study on the safety of the COVID-19 vaccines. One factor preventing such a study so far relates to challenges combining the different relevant data bodies on vaccination with suitable outcome data, specifically statutory health insurance claims data. Individual identifiers used across these data bodies are of unknown quality and reliability for data linkage.

View Article and Find Full Text PDF

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!