Literature DB >> 30429027

Use of health insurance data to identify and quantify the prevalence of main comorbidities in lung cancer patients.

David Jegou1, Cécile Dubois2, Viki Schillemans3, Sabine Stordeur4, Cindy De Gendt5, Cécile Camberlin6, Leen Verleye7, France Vrijens8.   

Abstract

BACKGROUND: Identifying comorbidities in lung cancer patients is a complex process in population-based studies and no gold standard exists. The current study aims to identify and measure the main comorbidities using administrative health insurance data, which were available on a population-based level.
METHOD: A literature search was conducted to identify comorbidities in lung cancer patients and to select Anatomical Therapeutic Chemical codes to measure them. For each patient, the volume of delivered relevant drugs for each comorbidity in the year preceding the diagnosis of lung cancer was computed, based on the Defined Daily Doses reimbursed. Case definition rules were set by comparing the identification of comorbidities via health insurance data with the reporting of them in the medical files in a sample of hospitals.
RESULTS: Four comorbidities were identified: chronic respiratory diseases, chronic cardiovascular diseases, diabetes mellitus and renal diseases. A very good to moderate agreement between the prevalence based on medical files versus health insurance data was obtained for diabetes mellitus (kappa = 0.83), chronic cardiovascular diseases (kappa = 0.64), chronic respiratory diseases (kappa = 0.48) but not for renal diseases (kappa = 0.22). Because only 27% of patients having renal diseases recorded in the medical files were identified using health insurance data, this comorbidity was not withheld. Among 12,839 lung cancer patients diagnosed in 2010-2011 in Belgium, 29.7% had chronic respiratory diseases, 57.5% had chronic cardiovascular diseases and 14.1% had diabetes mellitus. DISCUSSION: This study showed that it was possible to capture three major comorbidities in lung cancer patients using administrative health data, namely, diabetes mellitus, chronic cardiovascular diseases, and chronic respiratory diseases. However, the agreement was only moderate for the last one. A prerequisite for using this methodology is that administrative health data are available for all patients.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Comorbidities; Health insurance data; Lung cancer

Mesh:

Year:  2018        PMID: 30429027     DOI: 10.1016/j.lungcan.2018.10.002

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  2 in total

1.  Completeness and selection bias of a Belgian multidisciplinary, registration-based study on the EFFectiveness and quality of Endometrial Cancer Treatment (EFFECT).

Authors:  Joren Vanbraband; Nancy Van Damme; Gauthier Bouche; Geert Silversmit; Anke De Geyndt; Eric de Jonge; Gerd Jacomen; Frédéric Goffin; Hannelore Denys; Frédéric Amant
Journal:  BMC Cancer       Date:  2022-06-01       Impact factor: 4.638

2.  Real-World Estimation of First- and Second-Line Treatments for Diffuse Large B-Cell Lymphoma Using Health Insurance Data: A Belgian Population-Based Study.

Authors:  Willem Daneels; Michael Rosskamp; Gilles Macq; Estabraq Ismael Saadoon; Anke De Geyndt; Fritz Offner; Hélène A Poirel
Journal:  Front Oncol       Date:  2022-02-28       Impact factor: 6.244

  2 in total

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