Going beyond cost-effectiveness: analyzing routine mental healthcare data and stakeholders' perspectives to improve depression care

Almost one in five individuals will experience a depressive disorder during their life. Apart from the impact on patients’ lives, depressive disorders also have a considerable impact on society. Despite the availability of various evidence-based treatments for depression, most treatments are only moderately effective. This can lead to a long journey to find a suitable treatment.In her dissertation, Kaying Kan focuses on improving care for patients with depression by learning from past and current treatment practices. For this purpose, routinely collected mental healthcare data regarding patient characteristics, treatment and outcomes were used. Her studies reveal that the effectiveness of treatments using an algorithm-based care program for depression in clinical practice may verge on results obtained in randomized controlled trials. One study demonstrated that treatment costs of patients with a depression and a personality disorder were considerably higher compared with treatment costs of patients with depression and other psychiatric comorbidities. Importantly, it was also shown that patients and clinicians consider restoring social functioning and achieving personal goals relevant outcomes of treatment. However, patients make a clear distinction between outcomes in first versus recurrent depressions. Furthermore, the development of a data-driven decision-aid for depression to enhance shared decision-making was presented, and an alternative approach for healthcare priority setting, in which different stakeholders systematically assess cost-effective treatments on aspects like feasibility and acceptability.The studies described in the dissertation demonstrate the potential of using linked administrative data enriched with qualitative data for improving care for patients with depression

Patients' and clinicians' perspectives on relevant treatment outcomes in depression:qualitative study

BACKGROUND: Although symptomatic remission is considered the optimal outcome in depression, this is not always achieved. Furthermore, symptom indicators do not fully capture patients' and clinicians' perspectives on remission. Broader indicators of (partial) remission from depression should be considered. AIMS: To investigate relevant outcomes of depression treatment in specialist care from patients' and clinicians' perspectives and to investigate whether these perspectives differ from each other. METHOD: Three focus groups with 11 patients with depression and seven semi-structured interviews with clinicians were conducted exploring their perspectives on remission. All interviews were audio-recorded and transcribed verbatim. We analysed the transcripts thematically using the phenomenologist approach. RESULTS: Independently, both patients and clinicians perceived the following outcomes relevant: restoring social functioning and interpersonal relations, regaining quality of life and achieving personal goals. All clinicians emphasised symptom reduction and satisfaction with treatment as relevant outcomes, whereas the former was not an obvious theme in patients. Unlike clinicians, patients made a clear distinction between treatment outcomes in first versus recurrent/chronic depression. CONCLUSIONS: Classically defined study outcomes based on symptom resolution only partly reflect issues considered important by patients and clinicians in specialist depression treatment. Incorporating patients' and clinicians' perspectives in the development of measurable end-points makes them more suitable for use in trials and subsequent translation to clinical practice. Furthermore, evaluating patients' perspectives on treatment outcomes helps in the development of tailored interventions according to patients' needs

Real-World Treatment Costs and Care Utilization in Patients with Major Depressive Disorder With and Without Psychiatric Comorbidities in Specialist Mental Healthcare

BACKGROUND: The majority of patients with major depressive disorder (MDD) have comorbid mental conditions. OBJECTIVES: Since most cost-of-illness studies correct for comorbidity, this study focuses on mental healthcare utilization and treatment costs in patients with MDD including psychiatric comorbidities in specialist mental healthcare, particularly patients with a comorbid personality disorder (PD). METHODS: The Psychiatric Case Register North Netherlands contains administrative data of specialist mental healthcare providers. Treatment episodes were identified from uninterrupted healthcare use. Costs were calculated by multiplying care utilization with unit prices (price level year: 2018). Using generalized linear models, cost drivers were investigated for the entire cohort. RESULTS: A total of 34,713 patients had MDD as a primary diagnosis over the period 2000–2012. The number of patients with psychiatric comorbidities was 24,888 (71.7%), including 13,798 with PD. Costs were highly skewed, with an average ± standard deviation cost per treatment episode of €21,186 ± 74,192 (median €2320). Major cost drivers were inpatient days and daycare days (50 and 28% of total costs), occurring in 12.7 and 12.5% of episodes, respectively. Compared with patients with MDD only (€11,612), costs of patients with additional PD and with or without other comorbidities were, respectively, 2.71 (p < .001) and 2.06 (p < .001) times higher and were 1.36 (p < .001) times higher in patients with MDD and comorbidities other than PD. Other cost drivers were age, calendar year, and first episodes. CONCLUSIONS: Psychiatric comorbidities (especially PD) in addition to age and first episodes drive costs in patients with MDD. Knowledge of cost drivers may help in the development of future stratified disease management programs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40273-021-01012-x

The clinical effectiveness of an algorithm-guided treatment program for depression in specialized mental healthcare: A comparison with efficacy trials.

Background: Doubts exist on whether effects found in randomized controlled trials (RCTs) are directly generalizable to daily clinical practice. This study aimed (a) to investigate the effectiveness of treatment options within an algorithm-guided treatment (AGT) program for depression and compare their effectiveness with outcomes of efficacy trials and (b) to assess the relation between treatment continuity and outcomes. Methods: This naturalistic study linked treatment data from January 2012 to November 2014 from a Dutch mental healthcare provider, to routine outcome monitoring (ROM) data (N = 351). Effectiveness of the treatment options (pharmacotherapy, psychotherapy and their combination) was compared to the efficacy reported in the meta-analyses. We included treatment continuity as binary variable "early terminators versus completers of the recommended number of treatment sessions". Results: Remission rates for psychotherapy (38% [95% CI: 32-45]), pharmacotherapy (31% [95% CI: 22-42]) and combination therapy (46% [95% CI: 19-75]) were respectively lower, comparable, and comparable to those reported in the meta-analyses. Similarly, response rates were respectively lower (24% [95% CI: 19-30]), lower (21% [95% CI: 13-31]), and comparable (46% [95% CI: 19-75]) to meta-analyses results. A similar share of early terminators and completers achieved remission and response. Limitations: A substantial proportion of patients had incomplete ROM data after data linkage. Limited set of patient characteristics to check for selection bias. Conclusions: Despite the more heterogeneous patient population in clinical practice, the effectiveness of an AGT program, emphasizing strict guideline adherence, approached that found in RCTs. A fixed number of treatment sessions may not suit all individual patients