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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-014-0611-4) contains supplementary material, which is available to authorized users

Latent variables in discrete choice experiments

This paper describes and applies a general approach for incorporating factors with structural equations into models for discrete choice. The approach gives form to the covariance matrix in random coefficient models. The factors act directly on the random coefficients as unobserved attributes. The structural equations allow the factors to act on each other building structures that can represent a variety of concepts such as global heterogeneity and segmentation. The practical outcomes include parsimonious and identified models with rich covariances and better fit. Of greater interest is the ability to specify models that represent and test theory on the relationships between the taste heterogeneities for covariates and in particular between the attributes within a discrete choice experiment. The paper describes the general model and then applies it to a discrete choice experiment with seven attributes. Four competing specifications are evaluated, which demonstrates the ability of the model to be identified and parsimonious. The four specifications also demonstrate how competing a priori knowledge of the structure of the attributes used in the experiment can be empirically tested and evaluated. The outcomes include new behavioral insights and knowledge about choice and choice processes for the subject area of discrete choice experiments