Use of Medicare hospital and physician data to assess breast cancer incidence.
OBJECTIVE
Health claims data have the potential of being an inexpensive, timely, and nationally representative source of information about cancer. This study examined the utility of Medicare hospital and physician data as an independent source to identify incident breast cancer cases.


METHODS
Data came from Medicare and the National Cancer Institute's SEER cancer registries. From 1992, for women residing in the SEER states (n = 659,260), Medicare hospital and physician claims were reviewed to identify women with a breast cancer diagnosis on a claim (n = 6,784). These women were matched with women in the SEER data who had been diagnosed with breast cancer in 1992 (n = 3,230). The sensitivity, specificity, and positive predictive value (PPV) of the Medicare data were calculated. Logistic regression models were used to identified cancer related procedures reported to Medicare that could distinguish true cases from false positive cases. Predicted values from these models were included to create plots of sensitivity versus false positive rates and sensitivity versus PPV.


RESULTS
Medicare hospital data had 62% sensitivity, 99.9% specificity, and 88% PPV. Physician claims increased sensitivity by 14%, with specificity of 99.4%, and a PPV of 10%. Inclusion of additional cancer related diagnoses and procedures improved the ability to distinguish true cases from false positives, although the number of false positive cases remained high.


CONCLUSIONS
The Medicare data overall offer limited potential to assess breast cancer incidence, largely because of low sensitivity and poor PPV. The Medicare data may have utility to identify women undergoing selected breast cancer treatments. In addition, the data may be used to help registries focus case-finding efforts, particularly for persons undergoing cancer related treatments.
Warren
J L
JL
Applied Research Branch, National Cancer Institute, Bethesda, MD 20892-7344, USA.
Feuer
E
E
Potosky
A L
AL
Riley
G F
GF
Lynch
C F
CF
D001943:Breast Neoplasms / Q000453:epidemiology*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D015994:Incidence; D007297:Inpatients / Q000706:statistics & numerical data; D016015:Logistic Models; D016344:Medicare Part A / Q000706:statistics & numerical data*; D016345:Medicare Part B / Q000706:statistics & numerical data*; D012372:ROC Curve; D012042:Registries / Q000706:statistics & numerical data; D018426:SEER Program / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology