Modeling and estimating preferences over treatment programs for depression

Patients seeking treatment for a new episode of Major Depressive Disorder at a HMO mental health clinic were surveyed about their preferences over the characteristics of depression treatment programs: treatment effectiveness, use of anti-depressants, number of hours of psychotherapy per month, out-of-pocket cost, and the presence of three possible side effects (weight gain, reduced sex drive, and inability to orgasm). Each respondent also chose her preferred treatment from alternative depression treatment programs. Each treatment option varied in terms of the treatment characteristics noted above.; In the first paper, a discrete-choice random-utility framework is used to model and estimate preferences over depression treatment programs as a function of the characteristics of the treatment program and individual characteristics. How an individual trades off these treatment characteristics, including cost, is modeled as a function of severity of depression, income, age, gender, and previous experience with side effects. The study shows that there is a difference between the amount an individual is willing to pay to eliminate her depression and the amount that she must be paid to accept continued depression. There is significant variation, based on observable characteristics, in the amount that individuals will pay to avoid side effects. At sufficiently high costs in terms of money and side effects the model predicts that some individuals will choose no treatment.; The second paper outlines and implements a simple method for using attitudinal data to identify differences in depression treatment preferences. The estimated model indicates the presence of three groups that differ in their sensitivity to treatment costs and side effects. The model fit improves by allowing the probability of class membership to vary with observable individual characteristics. Individuals who are younger, male, or less educated are more likely to be sensitive to treatment costs and side effects. The technique could be applied in clinics to assist health care providers in identifying individuals who are at risk of not continuing treatment.