European Journal of Epidemiology

, Volume 23, Issue 10, pp 681–688 | Cite as

Is it possible to estimate the incidence of breast cancer from medico-administrative databases?

  • L. Remontet
  • N. Mitton
  • C. M. Couris
  • J. Iwaz
  • F. Gomez
  • F. Olive
  • S. Polazzi
  • A. M. Schott
  • B. Trombert
  • N. Bossard
  • M. Colonna
Cancer

Abstract

One approach to estimate cancer incidence in the French Départements is to quantify the relationship between data in cancer registries and data obtained from the PMSI (Programme de Médicalisation des Systèmes d’Information Médicale). This relationship may then be used in Départements without registries to infer the incidence from local PMSI data. We present here some methodological solutions to apply this approach. Data on invasive breast cancer for 2002 were obtained from 12 Départemental registries. The number of hospital stays was obtained from the National PMSI using two different algorithms based on the main diagnosis only (Algorithm 1) or on that diagnosis associated to a mention of “resection” (Algorithm 2). Considering registry data as gold standard, a calibration approach was used to model the ratio of the number of hospital stays to the number of incident cases. In Départements with registries, validation of the predictions was done through cross-validation. In Départements without registries, validation was done through a study of homogeneity of the mean number of hospital stays per patient. Cross-validation showed that the estimates predicted by the model were true with data extracted by Algorithm 1 but not by Algorithm 2. However, with Algorithm 1, there was an important heterogeneity between French Départements as to the mean number of hospital stays per patient, which had an important impact on the estimations. In the near future, the method will allow using medico-administrative data (after calibration with registry data) to estimate Départemental incidence of selected cancers.

Keywords

Breast cancer Incidence Cancer registries Claims database Prediction Statistical modelling 

Abbreviations

PMSI

Programme de Médicalisation des Systèmes d’Information Médicale

ICD-O

International Classification of Diseases-Oncology

References

  1. 1.
    Remontet L, Esteve J, Bouvier AM, Grosclaude P, Launoy G, Menegoz F, et al. Cancer incidence and mortality in France over the period 1978–2000. Rev Epidemiol Sante Publique. 2003;51:3–30.PubMedGoogle Scholar
  2. 2.
    Belot A, Grosclaude P, Bossard N, Jougla E, Benhamou E, Delafosse P, et al. Cancer incidence and mortality in France over the period 1980–2005. Rev Epidemiol Sante Publique. 2008;56:159–75. doi:10.1016/j.respe.2008.03.117.PubMedCrossRefGoogle Scholar
  3. 3.
    Cooper GS, Yuan Z, Stange KC, Dennis LK, Amini SB, Rimm AA. The sensitivity of Medicare claims data for case ascertainment of six common cancers. Med Care. 1999;37:436–44. doi:10.1097/00005650-199905000-00003.PubMedCrossRefGoogle Scholar
  4. 4.
    Freeman JL, Zhang D, Freeman DH, Goodwin JS. An approach to identifying incident breast cancer cases using Medicare claims data. J Clin Epidemiol. 2000;53:605–14. doi:10.1016/S0895-4356(99)00173-0.PubMedCrossRefGoogle Scholar
  5. 5.
    Ganry O, Taleb A, Peng J, Raverdy N, Dubreuil A. Evaluation of an algorithm to identify incident breast cancer cases using DRGs data. Eur J Cancer Prev. 2003;12:295–9. doi:10.1097/00008469-200308000-00009.PubMedCrossRefGoogle Scholar
  6. 6.
    Leung KM, Hasan AG, Rees KS, Parker RG, Legorreta AP. Patients with newly diagnosed carcinoma of the breast: validation of a claim-based identification algorithm. J Clin Epidemiol. 1999;52:57–64. doi:10.1016/S0895-4356(98)00143-7.PubMedCrossRefGoogle Scholar
  7. 7.
    McBean AM, Babish JD, Warren JL. Determination of lung cancer incidence in the elderly using Medicare claims data. Am J Epidemiol. 1993;137:226–34.PubMedGoogle Scholar
  8. 8.
    McBean AM, Warren JL, Babish JD. Measuring the incidence of cancer in elderly Americans using Medicare claims data. Cancer. 1994;73:2417–25. doi :10.1002/1097-0142(19940501)73:9>2417::AID-CNCR2820730927<3.0.CO;2-L.Google Scholar
  9. 9.
    McClish DK, Penberthy L, Whittemore M, Newschaffer C, Woolard D, Desch CE, et al. Ability of Medicare claims data and cancer registries to identify cancer cases and treatment. Am J Epidemiol. 1997;145:227–33.PubMedGoogle Scholar
  10. 10.
    Middleton RJ, Gavin AT, Reid JS, O’Reilly D. Accuracy of hospital discharge data for cancer registration and epidemiological research in Northern Ireland. Cancer Causes Control. 2000;11:899–905. doi:10.1023/A:1026543100223.PubMedCrossRefGoogle Scholar
  11. 11.
    Wang PS, Walker AM, Tsuang MT, Orav EJ, Levin R, Avorn J. Finding incident breast cancer cases through US claims data and a state cancer registry. Cancer Causes Control. 2001;12:257–65. doi:10.1023/A:1011204704153.PubMedCrossRefGoogle Scholar
  12. 12.
    Toniolo P, Pisani P, Vigano C, Gatta G, Repetto F. Estimating incidence of cancer from a hospital discharge reporting system. Rev Epidemiol Sante Publique. 1986;34:23–30.PubMedGoogle Scholar
  13. 13.
    Brackley ME, Penning MJ, Lesperance ML. In the absence of cancer registry data, is it sensible to assess incidence using hospital separation records? Int J Equity Health. 2006;5:12. doi:10.1186/1475-9276-5-12.PubMedCrossRefGoogle Scholar
  14. 14.
    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;95:829–33.PubMedGoogle Scholar
  15. 15.
    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;55:386–91. doi:10.1016/S0895-4356(01)00487-5.PubMedCrossRefGoogle Scholar
  16. 16.
    Uhry Z, Colonna M, Remontet L, Grosclaude P, Carre N, Couris CM, et al. Estimating infra-national and national thyroid cancer incidence in France from cancer registries data and national hospital discharge database. Eur J Epidemiol. 2007;22:607–14. doi:10.1007/s10654-007-9158-6.PubMedCrossRefGoogle Scholar
  17. 17.
    Carroll RJ, Ruppert D. Prediction and calibration. Transformation and weighting in regression. New York: Chapman & Hall; 1988. p. 51–62.Google Scholar
  18. 18.
    Davidian M, Giltinan DM. Analysis of assay data. Nonlinear models for repeated data. London: Chapman & Hall; 1995. p. 275–98.Google Scholar
  19. 19.
    Goldstein H. Multilevel statistical models. 3rd ed. London: Arnold; 2003.Google Scholar
  20. 20.
    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 Sante Publique. 2004;52:151–60. doi:10.1016/S0398-7620(04)99036-0.PubMedCrossRefGoogle Scholar
  21. 21.
    Carroll RJ, Ruppert D, Stefanski LA, Crainiceau CM. Measurement error in nonlinear models. 2nd ed. New York: Chapman & Hall/CRC; 2006.Google Scholar
  22. 22.
    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. J Clin Epidemiol (in press).Google Scholar
  23. 23.
    Cooper GS, Yuan Z, Jethva RN, Rimm AA. Use of Medicare claims data to measure county-level variation in breast carcinoma incidence and mammography rates. Cancer Detect Prev. 2002;26:197–202. doi:10.1016/S0361-090X(02)00056-9.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • L. Remontet
    • 1
    • 2
    • 3
    • 4
  • N. Mitton
    • 5
  • C. M. Couris
    • 6
    • 7
  • J. Iwaz
    • 2
    • 3
    • 4
  • F. Gomez
    • 8
  • F. Olive
    • 9
  • S. Polazzi
    • 6
    • 7
  • A. M. Schott
    • 6
    • 7
  • B. Trombert
    • 10
  • N. Bossard
    • 2
    • 3
    • 4
  • M. Colonna
    • 5
    • 11
  1. 1.Service de Biostatistique, Batiment 4DCentre Hospitalier Lyon SudPierre-BeniteFrance
  2. 2.Service de BiostatistiqueHospices Civils de LyonLyonFrance
  3. 3.Université de Lyon, Université Lyon IVilleurbanneFrance
  4. 4.Laboratoire Biostatistique SantéCNRS, UMR 5558Pierre-BeniteFrance
  5. 5.Registre des cancers de l’IsèreMeylanFrance
  6. 6.Pole Information Médicale Evaluation RechercheHospices Civils de LyonLyonFrance
  7. 7.Université de Lyon, Université Lyon IILyonFrance
  8. 8.Département d’Information MédicaleCentre Léon BérardLyonFrance
  9. 9.Département d’Information MédicaleCentre Hospitalier Universitaire de GrenobleGrenobleFrance
  10. 10.Service de Santé Publique et d’Information MédicaleCentre Hospitalier Universitaire de Saint-EtienneSaint-EtienneFrance
  11. 11.Réseau des registres de cancers FRANCIMToulouseFrance

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