A discrete-choice experiment to assess treatment modality preferences of patients with anxiety disorder

The Effects of Patient Preference on Clinical Outcome, Satisfaction and Adherence Within the Treatment of Anxiety and Depression: A Meta-Analysis

BACKGROUND

Taking patient preference into consideration has received increased attention in the last decades. We conducted a meta-analysis to estimate the effects of patient preference on clinical outcome, satisfaction and adherence regarding treatment of depression and anxiety.

METHODS

Pubmed, Embase, PsycINFO and Scopus were searched for (cluster) randomized controlled trials. Twenty-six randomized controlled clinical trials were included, comprising 3670 participants, examining the effect of patient preference regarding treatment of anxiety and depression on clinical outcome, satisfaction and/or adherence.

RESULTS

No effect of patient preference was found on clinical outcome [d = 0.06, 95% CI = (-0.03, 0.15), p = 0.16, n = 23 studies]. A small effect of patient preference was found on treatment satisfaction [d = 0.33, 95% CI = (0.08, 0.59), p = 0.01, n = 6 studies] and on treatment adherence [OR = 1.55, 95% CI = (1.28, 1.87), p < 0.001, n = 22 studies].

LIMITATIONS

Patient preference is a heterogeneous concept, future studies should strive to equalize operationalization of preference. Subgroup analyses within this study should be interpreted with caution because the amount of studies per analysed subgroup was generally low. Most studies included in this meta-analysis focused on patients with depression. The small number of studies (n = 6) on satisfaction, prevents us from drawing firm conclusions.

CONCLUSIONS

While this meta-analysis did not find a positive effect of considering patient preference on clinical outcome, it was associated with slightly better treatment satisfaction and adherence. Accommodating preference of patients with anxiety and depression can improve treatment.

TRIAL REGISTRATION

PROSPERO: CRD42020172556.

Treatment Preferences of Adolescents and Young Adults with Depressive Symptoms: A Discrete Choice Experiment

BACKGROUND

Depression in adolescents and young adults is common and causes considerable disease burden while hampering their development, leading to adverse consequences in later life. Although treatment is available, young people are a vulnerable group regarding uptake and completion of treatment. To improve this, insight into youth's preferences for treatment is essential.

OBJECTIVE

The aim of this study was to investigate patient preferences for depression treatment in a Dutch sample aged 16-24 years using a discrete choice experiment (DCE).

METHODS

The study was conducted in The Netherlands between October 2018 and June 2019, and included 236 adolescents and young adults with current depressive symptoms or previous treatment. The DCE included five attributes (treatment type, frequency of appointment, waiting time, effectiveness, evaluation of therapeutic alliance) with corresponding levels. Results were analysed using latent class analysis.

RESULTS

Results show a general preference for individual psychotherapy, treatment with high frequency, high effectiveness, short waiting time and a standard evaluation of the therapeutic alliance ('click' with the therapist) early in treatment. Latent class analysis revealed three different patterns of preferences regarding treatment type and willingness to engage in therapy. The first class showed a strong preference for individual therapy. The second class, including relatively older, higher educated and treatment-experienced participants, preferred high frequency treatment and was more open to different forms of therapy. The third class, including lower educated, younger and treatment-naïve adolescents showed reluctance to engage in therapy overall and in group therapy specifically.

CONCLUSION

In this DCE, three classes could be identified that share similar preferences regarding treatment effectiveness, waiting time and evaluation of the therapeutic alliance, but varied considerably in their preference for treatment type (individual, group, or combined psychotherapy) and their willingness to engage. The results from this study may inform mental health care providers and institutions and help optimize professional care for adolescents and young adults with depressive symptoms, improving engagement in this vulnerable group.

Psychosocial factors associated with treatment preference in mental health

BACKGROUND

Studies examining the effects of incorporating patients' preferences into treatment outcomes highlight their impact on crucial aspects such as reduced dropout rates and enhanced effectiveness. Recognizing individuals' rights to participate in decisions about their treatments underscores the importance of studying treatment preferences and the factors influencing these choices.

AIM

This study aims to identify treatment preferences (psychological, pharmacological, or combined) among a sample of patients and to discern the psychosocial and clinical factors influencing these preferences.

METHODS

A total of 2,133 individuals receiving care at a community mental health unit completed assessments on anxious-depressive symptoms, social and occupational adjustment, and their treatment preference. Data analysis was conducted using SPSS, with descriptive statistics, Chi-square tests, and one-way ANOVA applied.

RESULTS

Preferences for treatments were distributed as follows: Combined (49.8%), psychological (33%), and pharmacological (10.6%). Factors such as diagnosis, severity of depressive and anxious symptoms, and functional impact were related to treatment preference with a moderate effect size. Meanwhile, various sociodemographic factors correlated with the selected treatment, though with a weak effect size.

CONCLUSIONS

There is a pronounced preference for combined treatments. The significance of psychological treatments is evident, as four out of five participants favored them in their choices. Addressing these preferences calls for an exploration within the broader context of prescription freedom in mental health.

Patients' choice preferences for specialist outpatient online consultations: A discrete choice experiment

Background

Internet hospitals are multiplying with solid support from the Chinese government. In internet hospitals, specialist outpatient online consultations (SOOC) are the primary services. However, the acceptance and utilization rates of this service are still low. Thus, the study of patients' choice preferences for SOOC is needed.

Objective

To analyze the choice preference of patients' SOOC via a discrete choice experiment, understand the influence of each factor and promote the development of internet hospitals.

Methods

Via a discrete selection experiment, a total of 162 patients from two general hospitals and three specialized hospitals in Beijing were selected for the questionnaire survey. The choice preferences were analyzed by conditional logit regression.

Results

From high to low, patients' willingness to pay (WTP) for the attributes of SOOC is as follows: doctors' recommendation rate (β highly recommend = 0.999), the convenience of applying SOOC services (β Convenient = 0.760), the increasing ratio of medical insurance payment for online services compared to offline (β Increase by 10% = 0.545), and the disease's severity (β severe = -3.024). The results of the subgroup analysis showed differences in patient choice preference by age, whether the patients had chronic diseases, income, and medical insurance types.

Conclusion

Both price and nonprice attributes influence the choice preference of SOOC for patients. Among them, patients are more inclined to choose SOOC when doctors highly recommend it, when it is convenient to apply, when medical insurance increases by 10%, and when disease severity is mild. The current findings show the government and medical institutions formulate auxiliary policies and welfare strategies by clarifying core attributes and adjusting the levels of different attributes to improve patients' acceptance of SOOC. The utility of SOOC and the further development of internet hospitals are radically promoted.

The Public's Preferences for Psychological Interventions During the COVID-19 Pandemic: A Discrete Choice Experiment

Aims

To explore the public's preference for psychological interventions through a discrete choice experiment and to provide references for formulating psychological intervention policies and establishing psychological intervention procedures in response to public health emergencies.

Methods

This study is a discrete choice experiment. Attributes and levels were identified through literature reviews, in-depth interviews, focus group discussions, and expert consultations. Experimental design principles were applied to generate choice sets containing different attribute levels and develop a survey instrument. Convenience sampling was conducted nationwide, and 1,045 participants were investigated. A mixed logit model was used to evaluate the public's preferences.

Results

All attributes in our study were found to have a significant influence on the public's preferences for psychological interventions during the COVID-19 pandemic. The public's preferences for providers and duration were influenced by the public's levels of education and classifications. Furthermore, the most ideal scenario was found to be a one-on-one psychological intervention provided by family and friends through social network platforms, for which the frequency is twice per week, and the duration of each intervention is 0.5-1 h.

Conclusions

The public's preferences for psychological interventions during the COVID-19 pandemic are affected by the method, form, frequency, provider, and duration of interventions. Our findings provide references for the formulation of psychological intervention policies and the establishment of psychological intervention procedures in response to public health emergencies.

Patients’ and Psychologists’ Preferences for Feedback Reports on Expected Mental Health Treatment Outcomes: A Discrete-Choice Experiment

In recent years, there has been an increasing focus on routine outcome monitoring (ROM) to provide feedback on patient progress during mental health treatment, with some systems also predicting the expected treatment outcome. The aim of this study was to elicit patients’ and psychologists’ preferences regarding how ROM system-generated feedback reports should display predicted treatment outcomes. In a discrete-choice experiment, participants were asked 12–13 times to choose between two ways of displaying an expected treatment outcome. The choices varied in four different attributes: representation, outcome, predictors, and advice. A conditional logistic regression was used to estimate participants’ preferences. A total of 104 participants (68 patients and 36 psychologists) completed the questionnaire. Participants preferred feedback reports on expected treatment outcome that included: (a) both text and images, (b) a continuous outcome or an outcome that is expressed in terms of a probability, (c) specific predictors, and (d) specific advice. For both patients and psychologists, specific predictors appeared to be most important, specific advice was second most important, a continuous outcome or a probability was third most important, and feedback that includes both text and images was fourth in importance. The ranking in importance of both the attributes and the attribute levels was identical for patients and psychologists. This suggests that, as long as the report is understandable to the patient, psychologists and patients can use the same ROM feedback report, eliminating the need for ROM administrators to develop different versions.

Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures

To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation strategies. To understand individual preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling (RDS) approach to recruit participants from four universities in three countries to complete a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combination, can serve to increase the external validity of a study by enabling recruitment of populations underrepresented in sampling frames, thus allowing preference results to be more generalizable to targeted subpopulations. A total of 99 students or staff members were invited to complete the survey, of which 72% started the survey (n = 71). Sixty-three participants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies. The model estimates indicated that participants preferred mitigation strategies that resulted in lower COVID-19 risk (i.e. sheltering-in-place more days a week), financial compensation from the government, fewer health (mental and physical) problems, and fewer financial problems. The high response rate and survey engagement provide proof of concept that RDS and DCE can be implemented as web-based applications, with the potential for scale up to produce nationally-representative preference estimates.

Patients' preferences for the treatment of anxiety and depressive disorders: a systematic review of discrete choice experiments

Introduction: Matching available mental health services to patients' preferences, as well as is possible, may increase patient satisfaction and help increase adherence to certain treatments. This study systematically reviewed discrete-choice experiments (DCEs) on patients' preferences for treatment of depressive and anxiety disorders and assessed the relative importance of outcome, process and cost attributes to improve the current and future treatment situations.Methods: A systematic literature review using PubMed, EMBASE and PsychInfo was conducted to retrieve all relevant DCEs published up to 15 April 2019, eliciting patient preferences for treatment of depressive and anxiety disorders. Data were extracted using an extraction sheet, and attributes were classified into outcome, process and cost attributes. The relative importance of each attribute category was then assessed, and studies were evaluated according to their reporting quality, using validated checklists.Results: A total of 11 studies were identified for qualitative analysis. All studies received an aggregate score of 4 on the five-point PREFS checklist (Purpose, Respondents, Explanation, Findings and Significance). Most attributes were outcome related (52%), followed by process (42%) and cost (6%) attributes. Comparing the attribute categories and summing up the relative importance weights for each category within the studies, process attributes were ranked as most important, followed by cost and outcome attributes.Conclusions: In this systematic review, heterogeneous results were observed regarding the inclusion and framing of different attributes across studies. Overall, patients considered process and cost attributes to be more important than outcome attributes. Outcomes and process are important for patients, and thus clinicians should be particularly aware of this and take patients' preferences into account, although the attribute importance may depend on chosen attributes and related levels.

Using discrete choice experiments to develop and deliver patient-centered psychological interventions: a systematic review

Developing and/or tailoring psychological interventions to align with patient preferences is a critical component of patient-centered care and has the potential to improve patient engagement and treatment outcomes. Discrete choice experiments (DCEs) are a quantitative method of assessing patient preferences that offer numerous strengths (i.e., ability to account for trade-offs), but are not routinely incorporated into health psychology coursework, likely leaving many unaware of the potential benefits of this methodology. To highlight the potential applications of DCEs within health psychology, this systematic review synthesises previous efforts to utilise DCEs to inform the design of patient-centered psychological care, defined as interventions targeting psychological (e.g., depression, anxiety) or behavioural health (e.g., pain management, adherence) concerns. Literature searches were conducted in March 2017 and November 2019 for articles reporting on DCEs using the terms 'discrete choice', 'conjoint', or 'stated preference'. Thirty-nine articles met all inclusion criteria and used DCEs to understand patient preferences regarding psychosocial clinical services (n = 12), lifestyle behaviour change interventions (n = 11), HIV prevention and/or intervention services (n = 10), disease self-management programmes (n = 4), or other interventions (n = 2). Clinical implications as well as limitations and directions for future research are discussed.

A discrete-choice experiment to assess treatment modality preferences of patients with depression

AIMS

There is an increasing interest in understanding patients' preferences in the area of healthcare decision-making to better match treatment with patients' preferences and improve treatment uptake and adherence. The aim of this study was to elicit the preferences of patients with a depressive disorder regarding treatment modalities.

MATERIALS AND METHODS

In a discrete-choice experiment, patients chose repetitively between two hypothetical depression treatments that varied in four treatment attributes: waiting time until the start of treatment, treatment intensity, level of digitalization, and group size. A Bayesian-efficient design was used to develop 12 choice sets, and patients' preferences and preference variation was estimated using a random parameters logit model.

RESULTS

A total of 165 patients with depression completed the survey. Patients preferred short (over long) waiting times, face-to-face (over digital) treatment, individual (over group) treatment, and one session per week over two sessions per week or one session per 2 weeks. Patients disfavoured digital treatment and treatment in a large group. Waiting time and treatment intensity were substantially less important attributes to patients than face-to-face (vs digital) and group size. Significant variation in preferences was observed for each attribute, and sub-group analyses revealed that these differences were in part related to education.

LIMITATIONS

The convenience sample over-represented the female and younger population, limiting generalizability. Limited information on background characteristics limited the possibilities to explore preference heterogeneity.

CONCLUSION

This study demonstrated how different treatment components for depression affect patients' preferences for those treatments. There is significant variation in treatment preferences, even after accounting for education. Incorporating individual patients' preferences into treatment decisions could potentially lead to improved adherence of treatments for depressive disorders.