An Analysis of Cancer Registry Cost Data: Methodology and Results.

The Centers for Disease Control and Prevention initiated an economic analysis of the National Program of Cancer Registries (NPCR) in 2005 to estimate the true economic costs of operating a cancer registry, identify costs associated with registry activities, and evaluate the factors that may affect the efficiency of registry operations. We developed a Web-based NPCR cost assessment tool (NPCR-CAT) to collect activity-based cost data from all 48 NPCR registries. We collected data on registry funding, actual expenditures, and factors that may affect the efficiency of operating a central cancer registry. Key lessons learned during data collection and analysis include the importance of working closely with registry staff and balancing the need for standardized data elements with an understanding of individual registry characteristics. Our findings and lessons can be adapted to develop costing tools for other surveillance systems and cancer control programs, both domestically and internationally.

Using a discrete choice experiment to inform the design of programs to promote colon cancer screening for vulnerable populations in North Carolina.

BACKGROUND: Screening for colorectal cancer (CRC) is suboptimal, particularly for vulnerable populations. Effective intervention programs are needed to increase screening rates. We used a discrete choice experiment (DCE) to learn about how vulnerable individuals in North Carolina value different aspects of CRC screening programs. METHODS: We enrolled English-speaking adults ages 50-75 at average risk of CRC from rural North Carolina communities with low rates of CRC screening, targeting those with public or no insurance and low incomes. Participants received basic information about CRC screening and potential program features, then completed a 16 task DCE and survey questions that examined preferences for four attributes of screening programs: testing options available; travel time required; money paid for screening or rewards for completing screening; and the portion of the cost of follow-up care paid out of pocket. We used Hierarchical Bayesian methods to calculate individual-level utilities for the 4 attributes' levels and individual-level attribute importance scores. For each individual, the attribute with the highest importance score was considered the most important attribute. Individual utilities were then aggregated to produce mean utilities for each attribute. We also compared DCE-based results with those from direct questions in a post-DCE survey. RESULTS: We enrolled 150 adults. Mean age was 57.8 (range 50-74); 55% were women; 76% White and 19% African-American; 87% annual household income under $30,000; and 51% were uninsured. Individuals preferred shorter travel; rewards or small copayments compared with large copayments; programs that included stool testing as an option; and greater coverage of follow-up costs. Follow-up cost coverage was most frequently found to be the most important attribute from the DCE (47%); followed by test reward/copayment (33%). From the survey, proportion of follow-up costs paid was most frequently cited as most important (42% of participants), followed by testing options (32%). There was moderate agreement (45%) in attribute importance between the DCE and the single question in the post-DCE survey. CONCLUSIONS: Screening test copayments and follow-up care coverage costs are important program characteristics in this vulnerable, rural population.