Classifying function for health outcome and quality-of-life evaluation. Self- versus interviewer modes

Validity assessment and the underreporting of dysfunction have been major problems in health-related quality-of-life measurement, including collecting data for analysis by the General Health Policy Model, using the Quality of Well-being scale (QWB). This analysis compares the results of self- versus interviewer modes of measurement and short, direct-answer questions versus probing algorithms in the QWB. The comparisons are made in terms of 1) correlations; 2) aggregate frequencies; 3) individual subject classifications; and 4) the actual state, established using evidence from multiple sources. Despite extremely high correlations between QWB scores from the two modes (greater than 0.98), the lowest interviewer mode sensitivity (0.86) and predictive value dysfunctional (0.91) were substantially superior to the highest self-classification characteristics (0.66 and 0.73). In the populations studied, specificities and predictive values functional were equivalent (greater than 0.94) for the two modes. The probe pattern of the interviewer mode was also less susceptible to false reports of dysfunction. These results are consistent with the underreporting of dysfunction noted by several major investigations of health status measurement. The authors conclude that interviewer-administered instruments using question algorithms are necessary if health-related quality of life is to be measured with sufficient reliability and validity to evaluate major clinical trials and follow-up studies.