Methods Inf Med 2011; 50(02): 124-130
DOI: 10.3414/ME09-01-0064
Original Articles
Schattauer GmbH

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

B. Trombert Paviot
1   CHU de St Etienne, Département de Santé Publique et d’Information Médicale, Université de Saint-Etienne, France
,
F. Gomez
2   Département d’Information Médicale, Centre Léon Bérard, Lyon, France
,
F. Olive
3   Département d’Information Médicale, CHU de Grenoble, Grenoble, France
,
S. Polazzi
4   Hospices Civils de Lyon, Pole Information Médicale Evaluation Recherche, Université de Lyon, RECIF, EA 4129 Santé Individu Société, Lyon, France
,
L. Remontet
5   Hospices Civils de Lyon, Service de Biostatistique, Lyon – UMR CNRS 5558, Laboratoire Biostatistique Santé, Pierre-Bénite, France
6   Université de Lyon, Université Lyon I, Villeurbanne, France
,
N. Bossard
5   Hospices Civils de Lyon, Service de Biostatistique, Lyon – UMR CNRS 5558, Laboratoire Biostatistique Santé, Pierre-Bénite, France
6   Université de Lyon, Université Lyon I, Villeurbanne, France
,
N. Mitton
7   Registre des cancers de l’Isere, France
,
M. Colonna
7   Registre des cancers de l’Isere, France
8   FRANCIM, Toulouse, France
,
A.-M. Schott
4   Hospices Civils de Lyon, Pole Information Médicale Evaluation Recherche, Université de Lyon, RECIF, EA 4129 Santé Individu Société, Lyon, France
6   Université de Lyon, Université Lyon I, Villeurbanne, France
› Author Affiliations
Further Information

Publication History

received: 13 April 2009

accepted: 16 July 2009

Publication Date:
18 January 2018 (online)

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.

* This study was supported by grants from the Institut National du Cancer (INCA) and from the Canceropôle Lyon Auvergne Rhône-Alpes (CLARA).


 
  • References

  • 1 Rumeau-Rouquette C, Blondel B, Kaminski M, Bréart G. Epidémiologie: Méthodes et Pratique. Paris: Flammarion Médecine; 1993
  • 2 Colonna M, Hedelin G, Esteve J, Grosclaude P, Launoy G, Buemi A. et al. National cancer prevalence estimation in France. Int J Cancer 2000; 87 (02) 301-304.
  • 3 Gigli A, Mariotto A, Clegg LX, Tavilla A, Corazziari I, Capocaccia R. et al. Estimating the variance of cancer prevalence from population-based registries. Stat Methods Med Res 2006; 15 (03) 235-253.
  • 4 Capocaccia R, Colonna M, Corazziari I, De Angelis R, Francisci S, Micheli A. et al. Measuring cancer prevalence in Europe: the EUROPREVAL Project. Annals of Oncology 2002; 13: 831-839.
  • 5 Verdecchia A, Micheli A, Colonna M, Moreno V, Izarzugaza MI, Paci E. A comparative analysis of cancer prevalence in cancer registry areas of France, Italy and Spain. EUROPREVAL Working Group. Ann Oncol 2002; 13 (07) 1128-1139.
  • 6 Micheli A, Yancik R, Krogh V, Verdecchia A, Sant M, Capocaccia R. et al. Contrasts in cancer prevalence in Connecticut, Iowa, and Utah. Cancer 2002; 95 (02) 430-439.
  • 7 Colonna M, Danzon A, Delafosse P, Mitton N, Bara S, Bouvier A-M. et al. Cancer prevalence in France: time trend, situation in 2002 and extrapolation to 2012. European Journal of Cancer 2008; 44: 115-122.
  • 8 Tabata N, Ohno Y, Matsui R, Sugiyama H, Ito Y, Tsukuma H. et al. Partial cancer prevalence in Japan up to 2020: estimates based on incidence and survival data from population-based cancer registries. Jpn J Clin Oncol 2008; 3 (02) 146-157.
  • 9 Pisani P, Bray F, Parkin DM. Estimates of the worldwide prevalence of cancer for 25 sites in the adult population. Int J Cancer 2002; 97 (01) 72-81.
  • 10 Hill C, Doyon F. Cancer prevalence in France. Bull Cancer 2001; 88 (10) 1019-1022.
  • 11 Coldman AJ, McBride ML, Braun T. Calculating the prevalence of cancer. Stat Med 1992; 11 (12) 1579-1589.
  • 12 Capocaccia R, De Angelis R. Estimating the completeness of prevalence based on cancer registry data. Stat Med 1997; 16 (04) 425-440.
  • 13 Trombert Paviot B, Couris CM, Couray Targe S, Rodrigues JM, Colin C, Schott AM. Quality and usefulness of an anonymous unique personal identifier to link hospital stays recorded in French claims database. Rev Epidémiol Santé Publique 2007; 55 (03) 203-211.
  • 14 Thomas N, Zitouni J, Bocquier A, Lewandowski E, Finkel S, Favier O. et al. Evaluation of the hospital stays chaining quality: a precondition for studying health inequalities at local level. Congrès national des Observatoires régionaux de la santé, Marseille, 16-17 octobre 2008 http://www.congresors/inegalitesdesante.fr
  • 15 Quantin C, Binquet C, Bourquard K, Allaert FA, Gouyon B, Ferdynus C. et al. Assessment of the discriminating power of identifiers for record linkage. Rev Epidémiol Santé Publique 2004; 52 (05) 431-440.
  • 16 Schott AM, Hajri T, Colin C, Grateau F, Gilly FN, Tissot E. et al. Usefulness of the French DRG based information system (PMSI) in the measurement of cancer activity in a multidisciplinary hospital: the Hospices Civils de Lyon. Bull Cancer 2002; 89 (11) 969-973.
  • 17 Couris CM, Schott AM, Ecochard R, Colin C. A literature review to assess the use of claims databases in identifying cancer incident cases. Health Services and Outcomes Research Methodology 2003; 4: 49-63.
  • 18 Remontet L, Mitton N, Couris CM, Iwaz J, Gomez F, Olive F. et al. Is it possible to estimate the incidence of breast cancer from medico-administrative databases?. Eur J Epidemiol 2008; 23: 681-688.
  • 19 Couris CM, Polazzi S, Olive F, Remontet L, Bossard N, Gomez F. et al. Breast cancer incidence using administrative data: correction with sensitivity and specificity. Journal of Clinical Epidemiology 2009; 62: 660-666.
  • 20 Carre N. et al. Predictive value and sensibility of hospital discharge system (PMSI) compared to cancer registries for thyroid cancer (1999–2000). Rev Epidémiol Santé Publique 2006; 54: 367-376.
  • 21 Couris CM. et al. Method of correction to assess the number of hospitalized incident breast cancer cases based on claims databases. J Clin Epidemiol 2002; 55: 386-391.
  • 22 Ganry O. et al. Evaluation of an algorithm to identify incident breast cancer cases using DRGs data. Eur J Cancer Prev 2003; 12: 295-299.
  • 23 Freeman JL. et al. An approach to identifying incident breast cancer cases using Medicare claims data. J Clin Epidemiol 2000; 53 (06) 605-614.
  • 24 Paviot BT, Martin C, Clavel L, De Laroche G, Rodrigues JM. From DRG databases to an epidemiological observatory for colorectal cancer in a French small area oncology network. Stud Health Technol Inform 2003; 95: 829-833.
  • 25 Ministère de l’emploi et de la solidarité.. Circulaire n°106 du 22 février 2001 relative au chaînage des séjours en établissements de santé dans le cadre du programme de médicalisation des systèmes d’information (PMSI). Bulletin officiel; 2001. p 13.
  • 26 Trouessin G, Allaert FA. FOIN: a nominative information occultation function. Stud Health Technol Inform 1997; 43: 196-200.
  • 27 Titton M. et al. Follow-up of cancer treatment activities at the University Teaching Hospital in Dijon: the value of data from standardized discharge summaries. Santé Publique 2008; 20 (05) 411-423.
  • 28 Thabuis A. et al. Retrospective census of cancer between 1994 and 2002 around the municipal solid waste incinerator of Gilly-sur-Isère. Rev Epidémiol Santé Publique 2007; 55: 426-432.