Good technologies gone bad: how and why the cost-effectiveness of a medical intervention changes for different populations.

Cost-effectiveness (CE) ratios vary considerably, not only across interventions, but within single interventions. Using a simple decision-tree model of the treat-vs no-treat decision to organize the analysis, four potential errors leading to these within-treatment differences in CE ratios are identified. These errors arise from estimates relating to 1) prior probabilities of disease; 2) treatment efficacies; 3) costs of treatment; and 4) patient preferences. Systematic biases, where present, suggest overuse of medical interventions. For diagnostic tests, two additional potential sources of error are considered (using a simple decision tree incorporating both test and treat decisions). These involve 5) sensitivity and specificity of the diagnostic test and 6) inappropriate choice of "cutoff" to determine abnormal patients, in part arising from errors in estimating prior probability of disease.