Download PDF
Methods Inf Med 2006; 45(05): 515-522
DOI: 10.1055/s-0038-1634112
Original Article
Schattauer GmbH

Estimates of the Number of Cancer Patients Hospitalized in a Geographic Area Using Claims Data without a Unique Personal Identifier

C. M. Couris
1   Department of Medical Information, Hospices Civils de Lyon, Lyon, France
,
C. Gutknecht
2   Unit of biostatistics, Hospices Civils de Lyon, Lyon, France
,
R. Ecochard
2   Unit of biostatistics, Hospices Civils de Lyon, Lyon, France
,
B. Gelas-Dore
1   Department of Medical Information, Hospices Civils de Lyon, Lyon, France
,
T. Hajri
1   Department of Medical Information, Hospices Civils de Lyon, Lyon, France
,
C. Colin
1   Department of Medical Information, Hospices Civils de Lyon, Lyon, France
,
A.-M. Schott
1   Department of Medical Information, Hospices Civils de Lyon, Lyon, France
› Author Affiliations
Further Information

Publication History

Received: 14 March 2005

accepted: 13 November 2005

Publication Date:
07 February 2018 (online)

    Permissions and Reprints

Summary

Objective: In French national claims databases, claims are currently anonymous i.e. not linked to individual patients. In order to improve our estimate of the medical activity related to cancer in one French region, a statistical method was developed to use claims data to assess the number of cancer patients hospitalized in acute care.

Methods: This method used the medical and administrative information available in the claims (i.e. age, primary site, length of stay) to predict an average number of stays per patient, followed by a number of patients. It was based on a two-phase study design using an internal dataset which contained personal identifiers to estimate the model parameters.

Results: The predicted number of acute care patients hospitalized in one or several health care centers in one French region was 38,109 with a 95% predictive interval (37,990; 38,228) for the first six months of 2002. A prediction error of 24% was found.

Conclusion: We provide a good estimate of the morbidity in acute care hospitals using claims data that is not linked to individual patients. This estimate reflects the medical activity and can be used to anticipate acute care needs.

Keywords

Health care surveys - patient discharge - population surveillance - neoplasms - Poisson distribution
 

  • References

  • 1 Ministère de la santé, de la famille et des personnes handicapées. Décret n°2003-418 du 7 mai 2003 portant création d’une mission interministérielle pour la lutte contre le cancer. Journal Officiel n°107. Texte n°203.
    PubMedGoogle Scholar
  • 2 Connell FA, Diehr P, Hart LG. The use of large data bases in health care studies. Annu Rev Public Health 1987; 8: 51-74.
    CrossrefPubMedGoogle Scholar
  • 3 Ministère du travail et des affaires sociales. Arrêté du 22 juillet 1996 relatif au recueil et au traitement des données d'activité médicale. Journal Officiel du 26 juillet 1996
    PubMedGoogle Scholar
  • 4 Direction des hôpitaux. Circulaire n°303 du 24 juillet 1989 relative à la généralisation du programme de médicalisation des systèmes d’information et à l’organisation de l’information médicale dans les hôpitaux publics. Bulletin Officiel n°89. Texte 46
    PubMedGoogle Scholar
  • 5 Minvielle E, de Pouvourville G, Jeunemaître A. PMSI contôle de la qualité du codage. Gestions Hospitalières 1991; 302: 17-22.
    PubMedGoogle Scholar
  • 6 Jimeno MT, Eynaud L, Di Falco S, Guisiano B. Contrôle de qualité : la chasse aux erreurs de codage. Journal d’Economie Médicale 1995; 13 (06) 315-21.
    PubMedGoogle Scholar
  • 7 Lacour B, Laurent JF, Lenfant MH, Loeb A, Peuvrel P, Sauvage M. et al Clinical coding in oncology. Bull Cancer 2001; 88 (02) 209-18.
    PubMedGoogle Scholar
  • 8 Beaumel C, Doisneau L, Vatan M. La situation démographique en 2000. Mouvement de la population. Insee Résultats. Société 2002 Octobre 2002. (10).
    PubMedGoogle Scholar
  • 9 DRASS, Service, Statistiques. Statistiques et indicateurs de la santé et du social. Mémento 2000 Rhône-Alpes. vol 2000-13-D. Lyon; 2000
    PubMedGoogle Scholar
  • 10 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 (011) 969-73.
    PubMedGoogle Scholar
  • 11 Schott AM, Hajri T, Gelas-Dore B, Couris CM, Couray-Targe S, Trillet-Lenoir V. et al Analysis of the medical activity related to cancer in a network of multidisciplinary hospitals using claims databases, the reseau Concorde Oncology Network. Bull Cancer 2005; 92 (02) 169-78.
    PubMedGoogle Scholar
  • 12 Direction des hôpitaux. Circulaire n°105 du 22 février 2001 relative au chaînage des séjours en établissements de santé dans le cadre du programme de médicalisation des systèmes d’information.
    PubMedGoogle Scholar
  • 13 Quantin C, Bouzelat H, Allaert FAA, Benhamiche AM, Faivre J, Dusserre L. How to ensure data security of an epidemiological follow-up: quality assessment of an anonymous record linkage procedure. International Journal of Medical Informatics 1998; 49: 117-22.
    CrossrefPubMedGoogle Scholar
  • 14 Quantin C, Dusserre L, Foucher P, Allaert FA, Metral P, Bernard A. et al Proposition méthodologique pour l’utilisation du PMSI dans le suivi des cancers bronchiques. Santé publique 1993; 6: 16-25.
    PubMedGoogle Scholar
  • 15 Paviot BT, Martin C, Clavel L, De Laroche G, Rodrigues JM, Remontet L. et al From DRG databases to an epidemiological observatory for colorectal cancer in a French small area oncology network. Stud Health Technol Inform 2003; 95 (1 Pt 1) 829-33.
    PubMedGoogle Scholar
  • 16 Ministère des Affaires Sociales et de la Solidarité Nationale: Circulaire n° 119 du 4 octobre 1985, relative à la mise en place dans les établissements hospitaliers de résumé de sortie standardisées (RSS). Bulletin Officiel, n°. 85-24. BIS.
    PubMedGoogle Scholar
  • 17 SAS Software SAS Institute, version 8.02, SAS Campus Drive Cary, NC USA.
    PubMed
  • 18 Carroll RJ, Ruppert D, Stefanski LA. Measurement error in nonlinear models. London: Chapman & Hall; 1995
    Google Scholar
  • 19 Gao S, Hui SL, Hall KS, Hendrie HC. Estimating disease prevalence from two-phase surveys with non-response at the second phase. Stat Med 2000; 19 (016) 2101-14.
    CrossrefPubMedGoogle Scholar
  • 20 Couris CM, Colin C, Rabilloud M, Schott AM, Ecochard R. Method of correction to assess the number of hospitalized incident breast cancer cases based on claims databases. J Clin Epidemiol 2002; 55 (04) 386-91.
    CrossrefPubMedGoogle Scholar
  • 21 Couris CM, Rabilloud M, Colin C, Ecochard R. Two-phase study to assess the number of cases based on claims databases: characteristics of the validation data set. Methods Inf Med 2002; 41 (04) 349-56.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Couris CM, Foret-Dodelin C, Rabilloud M, Colin C, Bobin JY, Dargent D. et al Sensitivity and specificity of two methods used to identify incident breast cancer in specialized units using claims databases. Rev Epidemiol Santé Publique 2004; 52 (02) 151-60.
    PubMedGoogle Scholar
  • 23 Faustini A, Fano V, Sangalli M, Ferro S, Celesti L, Contegiacomo P. et al Estimating incidence of bacterial meningitis with capture-recapture method, Lazio Region, Italy. Eur J Epidemiol 2000; 16 (09) 843-8.
    CrossrefPubMedGoogle Scholar
  • 24 Pezzotti P, Piovesan C, Michieletto F, Zanella F, Rezza G, Gallo G. et al Estimating the cumulative number of human immunodeficiency virus diagnoses by cross-linking from four different sources. Int J Epidemiol 2003; 32 (05) 778-83.
    CrossrefPubMedGoogle Scholar
  • 25 Cook LJ, Olson LM, Dean JM. Probabilistic record linkage: relationships between file sizes, identifiers and match weights. Methods Inf Med 2001; 40 (03) 196-203.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 26 Contiero P, Tittarelli A, Tagliabue G, Maghini A, Fabiano S, Crosignani P. et al The EpiLink record linkage software: presentation and results of linkage test on cancer registry files. Methods Inf Med 2005; 44 (01) 66-71.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 27 Quantin C, Binquet C, Allaert FA, Cornet B, Pattisina R, Leteuff G. et al Decision analysis for the assessment of a record linkage procedure: application to a perinatal network. Methods Inf Med 2005; 44 (01) 72-9.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 28 Arellano MG, Weber GI. Issues in identification and linkage of patient records across an integrated delivery system. J Health Inf Manag 1998; 12 (03) 43-52.
    PubMedGoogle Scholar
  • 29 Freeman P, Robbins A. The U.S. health data privacy debate. Will there be comprehension before closure?. Int J Technol Assess Health Care 1999; 15 (02) 316-31.
    CrossrefPubMedGoogle Scholar
  • 30 Roos LL, Wajda A. Record linkage strategies. Part I: Estimating information and evaluating approaches. Methods Inf Med 1991; 30 (02) 117-23.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 31 Brenner H, Schmidtmann I. Effects of record linkage errors on disease registration. Methods Inf Med 1998; 37 (01) 69-74.
    Article in Thieme ConnectPubMedGoogle Scholar