Patients with Newly Diagnosed Carcinoma of the Breast Validation of a Claim-based Identification Algorithm

Leung, Kwan‐Moon; Hasan, Aliya; Rees, Karen S.; Parker, Robert G.; Legorreta, Antonio P.

doi:10.1016/s0895-4356(98)00143-7

Cited by 42 publications

(36 citation statements)

References 23 publications

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“…In a randomized clinical trial comparing sparing and nonsparing cyclosporin regimens, differences in graft survival were not apparent until more than 3 years after the intervention [36]. Although some may question the appropriateness of using administrative claims for outcomes research, many studies have validated these data with data from other sources [17,37,38]. Furthermore, high concordance was observed among prescribing rates of cyclosporin, tacrolimus, azathioprine, and mycophenolate mofetil reported to the OPTN and Medicare billing records [39].…”

Section: Discussionmentioning

confidence: 99%

The impact of human leukocyte antigen matching on transplant complications and immunosuppression dosage

Aydingöz¹,

Takemoto²,

Pinsky³

et al. 2007

Human Immunology

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Section: Discussionmentioning

confidence: 99%

The impact of human leukocyte antigen matching on transplant complications and immunosuppression dosage

Aydingöz¹,

Takemoto²,

Pinsky³

et al. 2007

Human Immunology

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“…Previous studies have evaluated the accuracy of cancer diagnoses in Medicare claims data including breast, colorectal, endometrial, lung, pancreatic, and prostate cancers [3][4][5][6][7][8][9], but they did not include less frequent cancers such as hematologic malignancies, and the agreement of diagnosis dates between claims data and cancer registry data has not been understood since only month and year of diagnosis are available in SEER data. We sought to develop various claims-based definitions for incident lymphoma and leukemia as well as breast, lung, colorectal, and stomach cancer and assessed the accuracy of these definitions in comparison with registry data including the accuracy of the date of the clinical cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Agreement of diagnosis and its date for hematologic malignancies and solid tumors between medicare claims and cancer registry data

Setoguchi

Solomon

Glynn

et al. 2007

Cancer Causes Control

122

163

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“…The French PMSI system (Programme de Médicalisa-tion des Systèmes d'Information Médicale), a medicoadministrative database similar to Medicare's DiagnosticRelated Groups (DRG) program, is nationwide available and represents an alternative to mortality data. The system, primarily dedicated to management as in several other countries, cannot provide direct access to incidence [3][4][5][6][7][8][9][10][11][12][13][14]. Thus, specific methods should be applied to correct the information given by that system through the use of registry data [3,15,16].…”

Section: Introductionmentioning

confidence: 99%

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

Remontet

Mitton²,

Couris

et al. 2008

Eur J Epidemiol

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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.

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Patients with Newly Diagnosed Carcinoma of the Breast Validation of a Claim-based Identification Algorithm

Cited by 42 publications

References 23 publications

The impact of human leukocyte antigen matching on transplant complications and immunosuppression dosage

The impact of human leukocyte antigen matching on transplant complications and immunosuppression dosage

Agreement of diagnosis and its date for hematologic malignancies and solid tumors between medicare claims and cancer registry data

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

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