The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement

By Eric I Benchimol, L. Smeeth, A. Guttmann, K. Harron, David Moher, I. Petersen, H. Sørensen, E. von Elm, S. Langan. PLoS Medicine, 2015

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Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.

Subfields: Economics and Econometrics, Oncology, Statistics and Probability

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Citations in the corpus

Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology (2019, methodology): “Selected relevant recommendations for epidemiological studies are the STROBE Statement [53], the STROBE-ME Statement [54], the RECORD Statement [50], STROSA [57] and the PRISMA Statement [51].”
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness (2020, methodology): “The RECORD reporting guidelines for studies using routinely collected health data already recommend providing detailed information to this effect, 55 and several potential solutions can facilitate this process, including code sharing and project curation platforms such GitHub.”
Challenges in administrative data linkage for research (2017, background): “Sharing of this information can support transparent reporting of studies using linked administrative data (Benchimol et al., 2015).”
Making sense of complexity in context and implementation: the Context and Implementation of Complex Interventions (CICI) framework (2017, methodology): “The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement.”
Acute kidney injury in patients hospitalized with COVID-19 (2020, neutral): “guidelines, Reporting of Studies Conducted Using Observational Routinely Collected Health Data (RECORD).(34,35)”
A Review of Generalizability and Transportability (2021, background): “…the importance of describing generalizability and transportability is recognized by different trial reporting guidelines (e.g., CONSORT, RECORD, STROBE), they provide no clear guidance on tests or estimation procedures (Schulz, Altman and Moher, 2010; Benchimol et al., 2015; von Elm et al., 2008).”
The Danish National Patient Registry: a review of content, data quality, and research potential (2015, methodology): “Keywords: epidemiological methods, medical record linkage, registries, research design, validation studies Introduction As the role of routine computerized health data in epidemiological research is growing,(1) there is a need to examine their strengths and limitations.(2,3) Typical shortcomings of ”