OBJECTIVES: Estimating prevalence rates for rare medical conditions such as renal cell carcinoma (RCC) at state level by age and sex is difficult due to the paucity of available data resources. Available information may be fragmented because of a lack of national level surveillance. The use of commercial medical claim data alone is insufficient for estimation because the use of these data tends to result in biased estimates due to business practices of managed care organization. METHODS: Invision Data Mart and the US census data were used to address this problem. The study inclusion criteria for defining RCC patients was age of 18 years or older without prior history of HIV/AIDS, HVB, or HVC diagnoses and had at least 2 outpatient medical claim with an associated ICD9 code of 189.0. First, we estimated prevalence rates for the medical conditions by state, age, and sex using ICD9 codes from the commercial data (2002-2010). Then, reanalyzed using post-stratification weights derived from the 2010 Census data to reflect the state, age, and sex distribution of the US population. RESULTS: The sum of the adjusted state population weights yielded a total that was similar to the 2010 US census data, and adjusted values suggest that the overall 2010 US RCC prevalence is approximately 85k. Since there is no state level prevalence information for RCC by age and sex available, an indirect comparison was made by comparing the overall prevalence from Kantar Health (CancerMpact®). The overall prevalence estimates were similar; Kantar Health: 86,853 versus Study Estimate: 84,712. CONCLUSIONS: This method produced prevalence rates that take important health care related factors into account in the estimation process. We recommend the use of this combined approach for the estimation of prevalence rates of rare disease conditions and procedures.