ADJUSTMENT OF CANCER INCIDENCE RATES FOR ETHNIC MISCLASSIFICATION

Although ethnic population counts measured by the United States Census are based on self-identification, the same is not necessarily true of cases reported to cancer registries. The use of different ethnic clas sification methods for numerators and denominators may therefore lead to biased estimates of cancer incidence rates. The extent of such misc lassification may be assessed by conducting an ethnicity survey of can cer patients and estimating the proportion misclassified using double sampling models that account for sample stratification. For two ethnic categories, logistic regression may be used to model self-identified ethnicity as a function of demographic variables and the fallible clas sification method. Incidence rates then may be adjusted for misclassif ication using regression results to estimate the number of cancer case s of a given age, sex, and site in each self-identified ethnic group. An example is given using this method to estimate ethnic misclassifica tion of San Francisco Bay area Hispanic cancer patients diagnosed in 1 990. Results suggest that the number of cancer cases reported as Hispa nic is an underestimate of the number of cases self-identified as Hisp anic, resulting in an underestimate of Hispanic cancer rates.