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Understanding the Factors Underlying Disparities in Cancer Screening Rates Using the Peters-Belson Approach: Results From the 1998 National Health Interview Survey

Rao, R Sowmya PhD*; Graubard, Barry I. PhD*; Breen, Nancy PhD†; Gastwirth, Joseph L. PhD‡

Medical Care:
Original Article

Background: Cancer screening rates vary substantially by race and ethnicity. We applied the Peters-Belson approach, often used in wage discrimination studies, to analyze disparities in cancer screening rates between different groups using the 1998 National Health Interview Survey.

Methods: A regression model predicting the probability of getting screened is fit to the majority group and then used to estimate the expected values for minority group members had they been members of the majority group. The average difference between the observed and expected values for a minority group is the part of the disparity that is not explained by the covariates

Results: The observed disparities in colorectal cancer screening (5.88%) and digital rectal screening (8.54%) between white and black men were explained fully by the difference in their covariate distributions. Only half of the disparity in the observed screening rates (13.54% for colorectal and 17.47% for digital rectal) between white and Hispanic men was explained by the difference in covariates between the groups. The entire disparity observed in mammography screening rates for black and Hispanic women (2.71% and 6.53%, respectively) compared with white women was explained by the difference in covariate distributions

Conclusions: We found that the covariates that explain the disparity in screening rates between the white and the black population do not explain the disparity between the white and the Hispanic population. Knowing how much of a health disparity is explained by measured covariates can be used to develop more effective interventions and policies to eliminate disparity.

Author Information

From the *Biostatistics Branch, Division of Cancer Epidemiology and Genetics and the †Health Services and Economics Branch, Applied Research Program, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland; and the ‡Department of Statistics, George Washington University, Washington, DC.

The research of Professor Gastwirth was supported in part by grant #SES0317956 from the National Science Foundation.

Reprints: Barry I. Graubard, PhD, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, 6120 Executive Boulevard, EPS 8024, MSC 7244, Bethesda, MD 20892-7244. E-mail:

© 2004 Lippincott Williams & Wilkins, Inc.