Cancer surveillance research requires estimates of the prevalence of cancer risk factors and screening for small areas such as counties. Two popular data sources are the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey conducted by state agencies, and the National Health Interview Survey (NHIS), an area probability sample survey conducted through face-to-face interviews. Both data sources have advantages and disadvantages. The BRFSS is a larger survey and almost every county is included in the survey, but it has lower response rates as is typical with telephone surveys and it does not include subjects who live in households with no telephones. On the other hand, the NHIS is a smaller survey, with the majority of counties not included; but it includes both telephone and nontelephone households, and has higher response rates. A preliminary analysis shows that the distributions of cancer screening and risk factors are different for telephone and nontelephone households. Thus, information from the two surveys may be combined to address both nonresponse and noncoverage errors. A hierarchical Bayesian approach that combines information from both surveys is used to construct county-level estimates. The proposed model incorporates potential noncoverage and nonresponse biases in the BRFSS as well as complex sample design features of both surveys. A Markov chain Monte Carlo method is used to simulate draws from the joint posterior distribution of unknown quantities in the model that uses design-based direct estimates and county-level covariates. Yearly prevalence estimates at the county level for 49 states, as well as for the entire state of Alaska and the District of Columbia, are developed for six outcomes using BRFSS and NHIS data from the years 1997-2000. The outcomes include smoking and use of common cancer screening procedures. The NHIS/BRFSS combined county-level estimates are substantially different from those based on the BRFSS alone. KEY WORDS: BRFSS; Cancer screening; Complex sample survey; Gibbs sampling; Hierarchical model; Mammography; NHIS; Pap smear; Simulation; Smoking.