2005
DOI: 10.1097/01.mlr.0000167176.41645.c7
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The Added Value of Claims for Cancer Surveillance

Abstract: Medicare data represent a valid resource for supplementing state cancer registries in surveillance efforts. This potential is especially applicable to cancers predominantly diagnosed and treated outside the hospital setting.

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Cited by 29 publications
(39 citation statements)
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“…Even in America, which does not have a universal coverage, various algorithms have been used to validate the usage of claims data (Nattinger. et al, 2004;Penberthy et al, 2005;Miller et al, 2009;Riley, 2009). For example, Medicare claims records, the Surveillance, Epidemiology, and End Results (SEER) Program, and clinical operative reports were compared to classify kidney cancer surgeries (Miller et al, 2009).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Even in America, which does not have a universal coverage, various algorithms have been used to validate the usage of claims data (Nattinger. et al, 2004;Penberthy et al, 2005;Miller et al, 2009;Riley, 2009). For example, Medicare claims records, the Surveillance, Epidemiology, and End Results (SEER) Program, and clinical operative reports were compared to classify kidney cancer surgeries (Miller et al, 2009).…”
Section: Discussionmentioning
confidence: 99%
“…This study concluded that Medicare data could be a supplement for the disease registry. Another study examined the algorithm developed to detect the incidence of breast cancer using Medicare claims data (Penberthy et al, 2005). Their developed algorithm showed a sensitivity of 80%, suggesting that Medicare claims data is useful in health services research.…”
Section: Discussionmentioning
confidence: 99%
“…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%
“…It should be noted that the accuracy rate increases from 88 % based only on the nonmatched cases to 95 % when both matched and nonmatched billingreported cases are included in the calculation. Previous studies have also demonstrated the potential for billing data to identify missed cancers and treatments from the physician office [2,4,5,[9][10][11]15]. With the implementation of the ICD-10 coding system, the specificity of billing data is likely to increase as the new system includes many additional detailed codes that more closely match the ICD-O coding system [46].…”
Section: Limitationsmentioning
confidence: 99%
“…Yet cancer surveillance data collection remains primarily hospital-based, even though cancer diagnosis and treatment are rapidly migrating to the outpatient setting [1][2][3][4][5][6][7][8]. The continued reliance on hospital-based reporting results in those cases diagnosed and/or treated exclusively in the physician office setting to be unreported or to have significant delays in reporting [2,[8][9][10][11][12]. Reporting of hematologic malignancies is an increasingly important focus for cancer surveillance because hematologic malignancies in particular may be diagnosed and treated in the outpatient setting, resulting in substantially higher rates of under-reporting of these cancers estimated to be as much as 18-37 % [13][14][15][16][17][18].…”
Section: Introductionmentioning
confidence: 99%