Linked Life Data

10880779

Source:http://linkedlifedata.com/resource/pubmed/id/10880779

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2000-7-27
pubmed:abstractText
This study developed and evaluated a method for ascertaining a newly diagnosed breast cancer case using multiple sources of data from the Medicare claims system. Predictors of an incident case were operationally defined as codes for breast cancer-related diagnoses and procedures from hospital inpatient, hospital outpatient, and physician claims. The optimal combination of predictors was then determined from a logistic regression model using 1992 data from the linked SEER registries-Medicare claims data base and a sample of noncancer controls drawn from the SEER areas. While the ROC curve demonstrates that the model can produce levels of sensitivity and specificity above 90%, the positive predictive value is comparatively low (67-70%). This low predictive value is largely the result of the model's limitation in distinguishing recurrent and secondary malignancies from incident cases and possibly from the model identifying true incident cases not identified by SEER. Nevertheless, the logistic regression approach is a useful method for ascertaining incident cases because it allows for greater flexibility in changing the performance characteristics by selecting different cut-points depending on the application (e.g., high sensitivity for registry validation, high specificity for outcomes research). It also allows us to make specific adjustments to population based estimates of breast cancer incidence with claims.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0895-4356
pubmed:author
pubmed:issnType
Print
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
605-14
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
An approach to identifying incident breast cancer cases using Medicare claims data.
pubmed:affiliation
Division of Geriatric Medicine, Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA. jfreeman@utmb.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.