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. 2011;50(2):124-30.
doi: 10.3414/ME09-01-0064. Epub 2010 Aug 5.

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

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

B Trombert Paviot et al. Methods Inf Med. .

Abstract

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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