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Abstract Details
Activity Number:
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75
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Health Policy Statistics
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Abstract - #302395 |
Title:
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An Algorithm for Predicting Oral and Pharyngeal Cancers Using the Seer-Medicare Linked Database
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Author(s):
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John David Keighley*+ and Jonathan D. Manhnken and Matthew Stuart Mayo
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Companies:
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University of Kansas Medical Center and University of Kansas Medical Center and University of Kansas Medical Center
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Address:
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Department of Biostatistics, Kansas CIty, KS, 66160,
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Keywords:
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secondary ;
data ;
analysis ;
health ;
outcomes ;
research
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Abstract:
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If a researcher wants to consider the full picture of treatment for a cancer patient they can not go to a record from a cancer registry. When data is transmitted using the North American Association of Central Cancer Registries (NAACCR ) format there is room for one code and one date for each of the six different types of treatment. It is possible to obtain a more complete picture of the treatment of a cancer patient using billing records if they are available. The most complete source available for this purpose is the SEER-Medicare Linked Database. SEER is considered to be the gold standard for cancer registries with a case reporting completeness of 98% of the cases diagnosed in their catchment area. This project developed a new method to identify oral and pharyngeal cancer cases using medicare ICD 9 diagnosis and procedure codes. The algorithm began using methods developed in record-linkage software and was completed in a new form. This method resulted in a sensitivity of over 94% and a specificity of over 96%. This method can be used to identify oral and pharyngeal cancer cases using Medicare records in non-SEER areas which then allows a reasearcher to consider treatment for the
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