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Published in: European Journal of Epidemiology 10/2008

01-10-2008 | Cancer

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

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

Published in: European Journal of Epidemiology | Issue 10/2008

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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.
Literature
1.
go back to reference 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.PubMed 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.PubMed
7.
go back to reference 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.PubMed 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.PubMed
8.
go back to reference 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. 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.
9.
go back to reference 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.PubMed 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.PubMed
10.
12.
go back to reference 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.PubMed 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.PubMed
13.
14.
go back to reference 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.PubMed 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.PubMed
16.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
17.
go back to reference Carroll RJ, Ruppert D. Prediction and calibration. Transformation and weighting in regression. New York: Chapman & Hall; 1988. p. 51–62. Carroll RJ, Ruppert D. Prediction and calibration. Transformation and weighting in regression. New York: Chapman & Hall; 1988. p. 51–62.
18.
go back to reference Davidian M, Giltinan DM. Analysis of assay data. Nonlinear models for repeated data. London: Chapman & Hall; 1995. p. 275–98. Davidian M, Giltinan DM. Analysis of assay data. Nonlinear models for repeated data. London: Chapman & Hall; 1995. p. 275–98.
19.
go back to reference Goldstein H. Multilevel statistical models. 3rd ed. London: Arnold; 2003. Goldstein H. Multilevel statistical models. 3rd ed. London: Arnold; 2003.
20.
go back to reference 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.PubMedCrossRef 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.PubMedCrossRef
21.
go back to reference Carroll RJ, Ruppert D, Stefanski LA, Crainiceau CM. Measurement error in nonlinear models. 2nd ed. New York: Chapman & Hall/CRC; 2006. Carroll RJ, Ruppert D, Stefanski LA, Crainiceau CM. Measurement error in nonlinear models. 2nd ed. New York: Chapman & Hall/CRC; 2006.
22.
go back to reference 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). 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).
Metadata
Title
Is it possible to estimate the incidence of breast cancer from medico-administrative databases?
Authors
L. Remontet
N. Mitton
C. M. Couris
J. Iwaz
F. Gomez
F. Olive
S. Polazzi
A. M. Schott
B. Trombert
N. Bossard
M. Colonna
Publication date
01-10-2008
Publisher
Springer Netherlands
Published in
European Journal of Epidemiology / Issue 10/2008
Print ISSN: 0393-2990
Electronic ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-008-9282-y

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