A comparison of various methods of collecting self-reported health outcomes data among low-income and minority patients.

In a randomized trial of different data collection methods, we challenged the untested assumption that reliable data cannot be obtained from lower-income and/or minority patients by self-administered questionnaires. We tested three methods of data collection among a sample of lower-income and minority patients (n = 697) in Indianapolis at a site for the Type II Diabetes Patient Outcomes Research Team. The study included a questionnaire literacy screening instrument to assess patients' functional literacy. Based on their functional literacy, patients were randomized to one of three methods of data collection: mail-out/mail-back, hand-out/assisted, or the in-home interview. We constructed a tiered system for reassigning nonresponders to alternative methods of data collection, using the in-home interview as the fall-back strategy. We compared the response rates, item completion rates, and internal consistency reliabilities of self-reported health status measures between patients with and without literacy limitations and across the three methods of data collection. Patients with and without literacy limitations, across methods of data collection, provided high-quality data, as evidenced by high item completion rates (> 84%) and high reliability assessments (internal consistency reliability coefficients > .80) for each health status measure. As part of the tiered study design, nonresponders randomized to either the mail-out/mail-back or the hand-out/assisted method were interviewed. These patients were significantly older, had significantly lower education and income levels, and had significantly poorer self-reported visual function as compared with those who responded to the originally assigned method. We conclude that expensive, labor-intensive data collection methods, such as in-home interviews, are not necessary for many low-income, minority patients to generate high-quality, reliable health status data. Using appropriate screening instruments, those patient subgroups needing special help can be screening instruments, those patient subgroups needing special help can be identified and targeted for more expensive data collection methods. This tiered approach has policy implications for the cost, feasibility, and quality of data collection in health outcomes research.