Archive (2016–2006)

Identifying Prevalent Cases of Breast Cancer in the French Case-mix Databases

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
DOI: https://doi.org/10.3414/ME09-01-0064
Issue: 2011 (Vol. 50): Issue 2 2011
Pages: 124-130

Identifying Prevalent Cases of Breast Cancer in the French Case-mix Databases

Original Article

B. Trombert Paviot (1), F. Gomez (2), F. Olive (3), S. Polazzi (4), L. Remontet (5, 6), N. Bossard (5, 6), N. Mitton (7), M. Colonna (8, 7), A.-M. Schott (5, 4)

(1) CHU de St Etienne, Département de Santé Publique et d’Information Médicale, Université de Saint-Etienne, France; (2) Département d’Information Médicale, Centre Léon Bérard, Lyon, France; (3) Département d’Information Médicale, CHU de Grenoble, Grenoble, France; (4) Hospices Civils de Lyon, Pole Information Médicale Evaluation Recherche, Université de Lyon, RECIF, EA 4129 Santé Individu Société, Lyon, France; (5) Université de Lyon, Université Lyon I, Villeurbanne, France; (6) Hospices Civils de Lyon, Service de Biostatistique, Lyon – UMR CNRS 5558, Laboratoire Biostatistique Santé, Pierre-Bénite, France; (7) Registre des cancers de l’Isere, France; (8) FRANCIM, Toulouse, France

Keywords

Hospital Information Systems, Epidemiology, prevalence, breast neoplasms, case mix

Summary

Objectives: Little is known about cancer prevalence due to a lack of systematic recording of cancer patient follow-up data. To estimate the annual hospital prevalence of breast cancer in the general population of the Isère department (1.1 million inhabitants) in the Rhône-Alpes region, the second largest region in France (6 million inhabitants), we used the inpatient case-mix data, available in most European countries, to develop a method of cancer case identification. Methods: A selection process was applied to the acute care hospital datasets among women aged 18 years or older, living in the Isère department and treated for breast cancer between 2004 and 2007. The first step in case selection was based on the national anonymous unique patient identifier. The second step consisted of retrieving all hospital stays for each case. The third step was designed to detect inconsistencies in the coding of the primary localization. An algorithm based on ICD-10 code for the hospital admission diagnosis was used to rule out hospitalizations unrelated to breast cancer. Five possible models for estimating prevalence were created combining selection steps with the admission diagnosis algorithm. Results: Hospital prevalence over the four-year period varied from 6073 breast cancer cases for the simplest model (first selection step without the admission diagnosis algorithm) to 4951 when the first selection step was associated with the breast cancer code as admission diagnosis. The model combining the third selection step with a breast cancer-specific admission reason provided 5275 prevalent cases. Conclusion: The last model seems more appropriate for case-mix-data coding. Selecting admission diagnosis improved specificity. Combining all hospital stays for each patient has improved diagnostic sensitivity.

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