Cargando…

Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study

OBJECTIVES: The French E3N-EPIC (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale-European Prospective Investigation into Cancer and Nutrition) cohort enrolled 98 995 women aged 40 to 65 years at inclusion since 1990 to study the main risk factors for cancer a...

Descripción completa

Detalles Bibliográficos
Autores principales: Nguyen, Yann, Salliot, Carine, Gusto, Gaëlle, Descamps, Elise, Mariette, Xavier, Boutron-Ruault, Marie-Christine, Seror, Raphaèle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937120/
https://www.ncbi.nlm.nih.gov/pubmed/31848174
http://dx.doi.org/10.1136/bmjopen-2019-033536
_version_ 1783483828825751552
author Nguyen, Yann
Salliot, Carine
Gusto, Gaëlle
Descamps, Elise
Mariette, Xavier
Boutron-Ruault, Marie-Christine
Seror, Raphaèle
author_facet Nguyen, Yann
Salliot, Carine
Gusto, Gaëlle
Descamps, Elise
Mariette, Xavier
Boutron-Ruault, Marie-Christine
Seror, Raphaèle
author_sort Nguyen, Yann
collection PubMed
description OBJECTIVES: The French E3N-EPIC (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale-European Prospective Investigation into Cancer and Nutrition) cohort enrolled 98 995 women aged 40 to 65 years at inclusion since 1990 to study the main risk factors for cancer and severe chronic conditions in women. They were prospectively followed with biennially self-administered questionnaires collecting self-reported medical, environmental and lifestyle data. Our objective was to assess the accuracy of self-reported diagnoses of rheumatoid arthritis (RA) and to devise algorithms to improve the ascertainment of RA cases in our cohort. DESIGN: A validation study. PARTICIPANTS: Women who self-reported an inflammatory rheumatic disease (IRD) were asked to provide access to their medical record, and to answer an IRD questionnaire. Medical records were independently reviewed. PRIMARY AND SECONDARY OUTCOME MEASURES: Positive predictive values (PPV) of self-reported RA alone, then coupled with the IRD questionnaire, and with a medication reimbursement database were assessed. These algorithms were then applied to the whole cohort to ascertain RA cases. RESULTS: Of the 98 995 participants, 2692 self-reported RA. Medical records were available for a sample of 399 participants, including 305 who self-reported RA. Self-reported RA was accurate only for 42% participants. Combining self-reported diagnoses to answers to a specific IRD questionnaire or to the medication reimbursement database improved the PPV (75.6% and 90.1%, respectively). Using the devised algorithms, we could identify 964 RA cases in our cohort. CONCLUSION: Accuracy of self-reported RA is poor but adding answers to a specific questionnaire or data from a medication reimbursement database performed satisfactorily to identify RA cases in our cohort. It will subsequently allow investigating many potential risk factors of RA in women.
format Online
Article
Text
id pubmed-6937120
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-69371202020-01-09 Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study Nguyen, Yann Salliot, Carine Gusto, Gaëlle Descamps, Elise Mariette, Xavier Boutron-Ruault, Marie-Christine Seror, Raphaèle BMJ Open Rheumatology OBJECTIVES: The French E3N-EPIC (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale-European Prospective Investigation into Cancer and Nutrition) cohort enrolled 98 995 women aged 40 to 65 years at inclusion since 1990 to study the main risk factors for cancer and severe chronic conditions in women. They were prospectively followed with biennially self-administered questionnaires collecting self-reported medical, environmental and lifestyle data. Our objective was to assess the accuracy of self-reported diagnoses of rheumatoid arthritis (RA) and to devise algorithms to improve the ascertainment of RA cases in our cohort. DESIGN: A validation study. PARTICIPANTS: Women who self-reported an inflammatory rheumatic disease (IRD) were asked to provide access to their medical record, and to answer an IRD questionnaire. Medical records were independently reviewed. PRIMARY AND SECONDARY OUTCOME MEASURES: Positive predictive values (PPV) of self-reported RA alone, then coupled with the IRD questionnaire, and with a medication reimbursement database were assessed. These algorithms were then applied to the whole cohort to ascertain RA cases. RESULTS: Of the 98 995 participants, 2692 self-reported RA. Medical records were available for a sample of 399 participants, including 305 who self-reported RA. Self-reported RA was accurate only for 42% participants. Combining self-reported diagnoses to answers to a specific IRD questionnaire or to the medication reimbursement database improved the PPV (75.6% and 90.1%, respectively). Using the devised algorithms, we could identify 964 RA cases in our cohort. CONCLUSION: Accuracy of self-reported RA is poor but adding answers to a specific questionnaire or data from a medication reimbursement database performed satisfactorily to identify RA cases in our cohort. It will subsequently allow investigating many potential risk factors of RA in women. BMJ Publishing Group 2019-12-16 /pmc/articles/PMC6937120/ /pubmed/31848174 http://dx.doi.org/10.1136/bmjopen-2019-033536 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Rheumatology
Nguyen, Yann
Salliot, Carine
Gusto, Gaëlle
Descamps, Elise
Mariette, Xavier
Boutron-Ruault, Marie-Christine
Seror, Raphaèle
Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title_full Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title_fullStr Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title_full_unstemmed Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title_short Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study
title_sort improving accuracy of self-reported diagnoses of rheumatoid arthritis in the french prospective e3n-epic cohort: a validation study
topic Rheumatology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937120/
https://www.ncbi.nlm.nih.gov/pubmed/31848174
http://dx.doi.org/10.1136/bmjopen-2019-033536
work_keys_str_mv AT nguyenyann improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT salliotcarine improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT gustogaelle improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT descampselise improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT mariettexavier improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT boutronruaultmariechristine improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy
AT serorraphaele improvingaccuracyofselfreporteddiagnosesofrheumatoidarthritisinthefrenchprospectivee3nepiccohortavalidationstudy