Predictors of Medicare costs in elderly beneficiaries with breast, colorectal, lung, or prostate cancer.
BACKGROUND
Determining the apportionment of costs of cancer care and identifying factors that predict costs are important for planning ethical resource allocation for cancer care, especially in markets where managed care has grown.


METHODS
This study linked tumor registry data with Medicare administrative claims to determine the costs of care for breast, colorectal, lung and prostate cancers during the initial year subsequent to diagnosis, and to develop models to identify factors predicting costs.


METHODS
Patients with a diagnosis of breast (n = 1,952), colorectal (n = 2,563), lung (n = 3,331) or prostate cancer (n = 3,179) diagnosed from 1985 through 1988.


RESULTS
The average costs during the initial treatment period were $12,141 (s.d. = $10,434) for breast cancer, $24,910 (s.d. = $14,870) for colorectal cancer, $21,351 (s.d. = $14,813) for lung cancer, and $14,361 (s.d. = $11,216) for prostate cancer. Using least squares regression analysis, factors significantly associated with cost included comorbidity, hospital length of stay, type of therapy, and ZIP level income for all four cancer sites. Access to health care resources was variably associated with costs of care. Total R2 ranged from 38% (prostate) to 49% (breast). The prediction error for the regression models ranged from < 1% to 4%, by cancer site.


CONCLUSIONS
Linking administrative claims with state tumor registry data can accurately predict costs of cancer care during the first year subsequent to diagnosis for cancer patients. Regression models using both data sources may be useful to health plans and providers and in determining appropriate prospective reimbursement for cancer, particularly with increasing HMO penetration and decreased ability to capture complete and accurate utilization and cost data on this population.
Massey Cancer Center, Medical College of Virginia, Virginia Commonwealth University, Richmond 23298, USA.
L
L
Penberthy
S M
SM
Retchin
M K
MK
McDonald
D K
DK
McClish
C E
CE
Desch
G F
GF
Riley
T J
TJ
Smith
B E
BE
Hillner
C J
CJ
Newschaffer
D000368:Aged; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008498:Medical Record Linkage; D006278:Medicare / Q000191:economics*; D017059:Models, Econometric; D009369:Neoplasms / Q000191:economics* / Q000453:epidemiology; D018426:SEER Program / Q000706:statistics & numerical data; D014481:United States / Q000453:epidemiology