We used conjoint analysis to study group preferences as well as individual level preferences for depression treatment features. In addition, we were able to learn what might be associated with preferences for the attribute “treatment type” (medicine versus counseling) and predict how the choice of treatment type would change given different treatment options. Using conjoint methods we found that participants preferred counseling over medication, avoided options with severe side effects and wanted to be seen in the primary care doctor’s office as opposed to other venues. With respect to individual level treatment choice, we found a roughly bimodal distribution corresponding to a group of individuals who strongly preferred counseling and another smaller group who strongly preferred medication. In response to an open-ended question about what features of treatment were important (beyond those measured with the task), persons who had a stronger preference for counseling were more likely to bring up concerns about dependence on medication, effectiveness of treatment and issues related to side effects. Individuals who had a stronger preference for medication were more likely to bring up concerns about time and the therapeutic relationship with the provider and were also more likely to wonder about combining medication and counseling. Individuals who preferred counseling and individuals who preferred medication were equally likely to bring up insurance and cost as other important factors to consider when starting a depression treatment. Finally, we used the model to predict how the preferred treatment option changed when treatment attributes were varied and found that as the side effect of nausea changed from mild to moderate to severe, more participants would select counseling. Taken together, the way we used conjoint analysis in this study exemplifies a method for delineating how people make decisions about depression treatment, which individuals have a strong preference for one treatment attribute versus another, and what other treatment-related issues are associated with strong preferences for certain attributes of treatment.
Conjoint analysis allowed us to observe the influence of specific service features on overall choice. The group relative preference weights represent the value respondents associated with particular attributes of treatment. As opposed to simply asking individuals about their preferences for treatment, conjoint analysis can help to uncover what treatment features drive treatment preferences. Isolating what aspects of treatment are desired (and which attributes of treatment are aversive) can help determine ways in which treatments or communication about treatments may need to be adapted to achieve the patient-centered care to which the Quality Chasm report aspires.
In addition to calculating group level relative preference weights, we estimated the individual level relative preference weights for treatment choice. The method we used to calculate individual-level relative preference weights allowed us to examine the distribution of individual relative preference weights to learn about groups of persons within the sample with strong preferences for specific attributes. Specifically, we focused attention on individuals on the ends of the distribution of the relative preference weights associated with preference for counseling or medicine. Ascertaining which individuals have strong preferences for one or the other, linked to treatment, can be a first step in designing patient-centered care. For example, patients who strongly favor having treatment in the primary care doctor’s office might be targeted for a primary care-based intervention while others for whom other features of treatment are compelling, may find other venues acceptable. Further work will determine if individual preferences for treatment attributes are associated with patient characteristics (such as age, gender or comorbid illnesses) as well as whether preferences predict treatment uptake and response.
Our finding that counseling was preferred by most of the participants is consistent with other studies that have looked at preferences for counseling versus medication. [48
] However, our analyses allowed us to begin to understand more about some of the reasons underlying preference and to predict what might make people switch from desiring counseling to medication, or vice versa
. This is especially important for the primary care setting where a patient’s particular treatment preference (such as counseling) may not be readily available, but desired attributes of a treatment (e.g. minimizing side effects) may be feasibly addressed by the physician or through repackaging the way depression treatment is delivered (e.g. counseling in the primary care doctor’s office).
Group relative preference weights did not markedly differ when depression was described as mild or as severe. However, our model allowed us to estimate how proportions of individuals choosing medicine compared to counseling changed as the nausea as a drug side effect was described as mild, moderate, or severe. The proportion of individuals choosing counseling changed most markedly when the side effect was described as severe (proportion choosing counseling increased from 50% for mild nausea from drug to 85% for severe nausea). We asked open-ended questions to obtain information on features of treatment that might be important to respondents that were not included in our conjoint task. We learned that respondents with stronger preferences for counseling had concerns about side-effects and dependency on medication while persons with stronger preferences for medicines cited time and the relationship with the provider as important considerations, and expressed interested in combining treatments.
Before discussing the implications of our study, several limitations deserve comment. First, the participants represented a convenience sample and may react to the hypothetical scenarios very differently than would actual patients. Furthermore, people actually confronting these types of treatment decisions (i.e. patients suffering from depression) might respond very differently to the hypothetical scenarios. Second, with respect to the nature of our conjoint design, side effect severity and type of side effect were only associated with medicine. Had we included attributes related to side effects for counseling we may have seen a different response vis-à-vis the “type of treatment” attribute. In addition, other attributes not included such as cost might have been highly influential. The number and definition of attributes and levels is the critical step in any conjoint analysis task and was based on patient and expert opinion of the most important attributes to include. More work is needed to determine the relative effect of other important attributes on choice tasks.
Conjoint methods sharpen the focus on “what it is about treatment” that drives preferences and provides specific guideposts for how to design packages of treatment that are patient-centered. We plan to study whether there are features of treatment that are desired by depressed patients that could lead to new packages of treatments. Studying how preferences for attributes of treatment are related to treatment adherence, how preferences change over time as depression severity changes and how preferences change with treatment experience are important next steps. In addition, it will be important to look at how the valued attributes of different depression treatments vary among different groups of patients such as persons with co-morbid mental and physical illnesses or the elderly. Conjoint analysis may prove useful in re-thinking how mental health is delivered within primary care. Conjoint analysis has been successfully applied to organizational or service redesign to match with changing consumer needs[51
] and is increasingly being considered in medical service redesign.[53
] For example, conjoint analysis could be used to link patient preferences for specific attributes of both conventional treatments (e.g. medication and/or counseling) and non-conventional depression treatments (such as meditation or spiritual therapy) to observed behavior (initiation and adherence to prescribed treatment). If patients with preferences for specific levels of non-conventional treatment attributes are more likely to be non-adherent to prescribed treatments, then conventional treatments might be adapted to incorporate the desired attributes of non-conventional treatments (e.g. counseling that incorporates aspects of spirituality).