Breast cancer incidence using administrative data: correction with sensitivity and specificity

Couris, Chantal Marie; Polazzi, Stéphanie; Olive, F.; Remontet, Laurent; Bossard, Nadine; Gómez, F.; Schott, Anne-Marie; Mitton, N.; Colonna, Marc; Trombert, Béatrice

doi:10.1016/j.jclinepi.2008.07.013

Cited by 67 publications

(60 citation statements)

References 37 publications

(16 reference statements)

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“…We identified the definitions which were capable of identifying breast cancer cases from the claims database with high sensitivity, specificity, and PPV. Because we had no information on the Japanese codes used in the claims of breast cancer patients prior to the construction of definitions, we developed exploratory definitions by reference to previous studies [10][11][12][13][14][15][16][17][18][19] and using advice from experts in the area. We then evaluated each definition and attempted to determine the optimal combination of three validity measures (sensitivity, specificity, and PPV).…”

Section: Discussionmentioning

confidence: 99%

“…[10][11][12][13][14][15][16][17][18][19] Freeman et al 17) obtained a high sensitivity (90%) and specificity (99%), but a low PPV (70%). In their discussion, they attributed the high sensitivity to the use of three kinds of claims data (inpatients, outpatients, and physician services), while the low PPV was considered to be due to limitations in distinguishing recurrent and secondary cancers.…”

Section: Discussionmentioning

confidence: 99%

“…Setoguchi et al 10) developed case definitions to identify 6 types of cancer cases using multiple combinations of predictors in Pennsylvania, using Medicare claims data linked to pharmacy dispensing data and the population-based cancer registry. Couris et al 19) found that an algorithm consisting of diagnosis and breast cancer-related surgery codes could identify a similar number of the cases reported by the cancer registry.…”

mentioning

confidence: 97%

“…[10][11][12][13][14][15][16][17][18][19] For example, Freeman et al 17) determined the optimal combination of predictors (e.g., diagnosis, procedures) using a logistic regression model to identify the incidence of breast cancer from a population-based cancer registry and Medicare claims data, which included data of inpatients, outpatients, and physician services. Setoguchi et al 10) developed case definitions to identify 6 types of cancer cases using multiple combinations of predictors in Pennsylvania, using Medicare claims data linked to pharmacy dispensing data and the population-based cancer registry.…”

mentioning

confidence: 99%

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The Accuracy of Japanese Claims Data in Identifying Breast Cancer Cases

2015

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In use of a claims database for a study, an inaccurate diagnosis of breast cancer based on claims data may lead to invalid study results. The aim of this study was to assess the accuracy of definitions for identifying breast cancer cases from the Japanese claims database. The study cohort consisted of women with no prior cancer-related history, from the claims data at a single institution between January 1 and December 31, 2011. We developed 14 definitions for identifying breast cancer based on claims data, using a combination of diagnosis codes and treatment procedure codes. We calculated the sensitivity, specificity, and positive predictive value (PPV) of each definition, compared to cases identified from the standardized hospital-based cancer registry as a standard reference. A total of 50056 women were included in the study cohort from the claims database. We identified 633 breast cancer cases from the cancer registry. Of 14 definitions, 12 exhibited higher sensitivity than 90%, while the others exhibited lower sensitivity than 40%. The specificities of all definitions were high (≥99%), and the PPVs were between 65.8 and 90.7%. We selected the most optimal definition obtained from combinations of diagnosis and cancer treatment codes (surgery, chemotherapy, medication, radiation procedure), which had high values for sensitivity (90.4%), specificity (99.8%), and PPV (87.3%). Definitions obtained via combinations of the diagnosis codes and procedure codes could be used to accurately identify breast cancer cases from the claims database. Further studies in a multi-institutional setting are planned to confirm our results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

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The Accuracy of Japanese Claims Data in Identifying Breast Cancer Cases

2015

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“…Lifestyle changes and improvement in diagnosis seem to be the causes for this increase. In contrast overall incidence of breast cancer is declining in the United States in the last decade (Morabia et al, 2002;Couris et al, 2009). …”

Section: Discussionmentioning

confidence: 99%