A Choice-Based Conjoint Analysis of the Psychiatrist Decision-Making Process Used in Determining When to Discharge Adults With Major Depressive Disorder Hospitalized for Active Suicidal Ideation With Intent

ABSTRACT

To ascertain the relative importance of attributes considered when deciding to discharge patients hospitalized with major depressive disorder (MDD) and active suicidal ideation with intent, a choice-based conjoint analysis was conducted via online survey among US-based psychiatrists actively managing such patients. Potential attributes and attribute levels were identified. Attribute importance in decision to discharge and the discharge time frame were assessed. One hundred psychiatrists completed the survey. The relative importance of attributes were current MDD severity (relative importance weight [out of 100] 24.8 [95% confidence interval, 23.3-26.3]), clinician assessment of current suicidal ideation (20.8 [18.5-23.0]), previous history of suicide attempts (16.7 [15.9-17.6]), psychosocial support at discharge (13.0 [11.7-14.4]), postdischarge outpatient follow-up (9.8 [8.8-10.8]), current length of hospital stay (9.2 [8.1-10.3]), and suicidal ideation at admission (5.7 [4.8-6.6]). Thus, current clinical symptoms were considered the most important attributes by psychiatrists when discharging patients initially hospitalized with MDD and active suicidal ideation with intent.

Direct and Indirect Costs Among Caregivers of Patients With Major Depressive Disorder and Suicidal Ideation or Suicidal Attempt

Objective: To compare direct and indirect costs among caregivers of patients with major depressive disorder (MDD) and suicidal ideation and/or suicide attempts (MDSI) versus caregivers of patients with MDD alone versus caregivers of patients without MDD or suicidal ideation and/or suicide attempts (controls).

Methods: Cohorts were based on caregivers of adult patients with MDSI, MDD alone, and controls. Patients were identified by Workpartners employer database ICD-9/ICD-10 codes (January 2010 to July 2019) and were spouses or domestic partners of employees (caregivers). Twenty controls and 20 MDD-alone caregivers were matched to each MDSI caregiver on sex, age, and index year. All caregiver-patient pairs had 6 months pre/postindex information and met additional inclusion/exclusion criteria. Patient and caregiver medical and prescription claims and caregiver absenteeism (payment/time) were analyzed. Direct costs (medical, prescription) and indirect costs (absence payments by benefit type) were analyzed using separate, 2-part stepwise regression models and controlling for demographics, job-related variables, region, index year, and Charlson Comorbidity Index score.

Results: 570 MDSI caregiver-patient pairs and 11,400 matched MDD-alone and control pairs were identified. MDSI and MDD-alone caregivers had higher medical costs compared with controls ($5,131 and $4,548 versus $3,885, respectively; P < .0001). Prescription costs were highest among MDSI caregivers, followed by MDD-alone and control caregivers ($1,852, $1,425, and $1,005, respectively; P < .001). MDSI caregivers had the highest total indirect costs. MDSI patient medical and prescription costs were highest, followed by MDD-alone and control patients.

Conclusion: MDSI caregivers had significantly greater direct and indirect costs compared with MDD-alone and non-MDD caregivers.

How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?

OBJECTIVES

To create and validate a model to predict depression symptom severity among patients with treatment-resistant depression (TRD) using commonly recorded variables within medical claims databases.

METHODS

Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)-9 record on or after the index TRD date were identified (2013-2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ-9 total score category (score: 0-9 = none to mild, 10-14 = moderate, 15-27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model.

RESULTS

Among 5,356 PHQ-9 scores in the study population, the mean (standard deviation) PHQ-9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range.

CONCLUSIONS

While there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population-level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.

Economic burden of treatment-resistant depression in privately insured US patients with co-occurring anxiety disorder and/or substance use disorder

OBJECTIVE

To assess the burden of treatment-resistant depression (TRD) among privately insured patients with anxiety disorder and/or substance use disorders (SUD).

METHODS

Adults <65 years old were identified in the Optum Health Care Solutions Inc. database (July 2009-March 2017). Among those with major depressive disorder (MDD) and antidepressant use, patients who initiated a third antidepressant (index date) after two regimens at adequate dose and duration were classified in the TRD cohort and patients without evidence of TRD were classified in the non-TRD MDD control cohort. The non-MDD control cohort comprised patients without MDD. In the non-TRD MDD and non-MDD cohorts, the index date was imputed to mimic the distribution of time in the TRD cohort from the first antidepressant to the index date or from the start of eligibility to the index date, respectively. Patients with <6 months of continuous insurance eligibility pre-/post-index, psychosis, schizophrenia, bipolar disorder and related conditions, dementia, and development disorders, and/or no baseline anxiety disorder and/or SUD were excluded. Patients with TRD were matched 1:1 to patients with non-TRD MDD and patients without MDD, based on exact matching factors (i.e. availability of work loss data) and propensity scores computed based on characteristics measured pre-index. Outcomes, including healthcare resource use (HRU) and costs, work productivity loss and related costs measured per patient per year ≤24 months post-index were compared between matched TRD, non-TRD MDD and non-MDD cohorts.

RESULTS

A total of 3166 patients were identified in the TRD cohort and matched to non-TRD MDD and non-MDD cohorts. Among patients with TRD (mean age 39 years, 60.5% female), 87.3% had an anxiety disorder, 24.1% had SUD. The TRD cohort had higher HRU vs non-TRD MDD and non-MDD cohorts: 0.32 vs 0.20 and 0.14 inpatient admissions, 0.91 vs 0.73 and 0.58 emergency department visits, and 23.8 vs 16.8 and 11.6 outpatient visits, respectively (all p < .01). The TRD cohort had higher healthcare costs ($16,674) vs non-TRD MDD ($10,945) and non-MDD ($6493) cohorts (all p < .01). Among patients with work loss data (N = 310/cohort), patients with TRD had more work loss days (54) and higher work loss-related costs ($13,674) vs patients with non-TRD MDD (32 days; $7131) and without MDD (17 days; $4798; all p < .01).

CONCLUSIONS

In patients with an anxiety disorder and/or SUD, TRD was associated with higher HRU, healthcare costs, work loss days and work loss-related costs.

Economic Burden of Treatment-Resistant Depression in Privately Insured U.S. Patients with Physical Conditions

BACKGROUND

Little is known about the economic burden of treatment-resistant depression (TRD) in patients with physical conditions.

OBJECTIVE

To assess health care resource utilization (HRU) and costs, work loss days, and related costs in patients with TRD and physical conditions versus patients with the same conditions and non-TRD major depressive disorder (MDD) or without MDD.

METHODS

Adults aged < 65 years with MDD treated with antidepressants were identified in the OptumHealth Care Solutions database (July 2009-March 2017). Patients who received a diagnosis of MDD and initiated a third antidepressant regimen (index date) after 2 regimens of adequate dose and duration were defined as having TRD. Patients with non-TRD MDD and without MDD were assigned a random index date. Patients with < 6 months of continuous health plan eligibility pre- or post-index; a diagnosis of psychosis, schizophrenia, bipolar disorder/mania, dementia, and developmental disorders; and/or no baseline physical conditions (cardiovascular, metabolic, and respiratory disease or cancer) were excluded. Patients with TRD were matched 1:1 to each of the non-TRD MDD and non-MDD cohorts based on propensity scores. Per patient per year HRU, costs, and work loss outcomes were compared up to 24 months post-index date using negative binominal and ordinary least square regressions.

RESULTS

A total of 2,317 patients with TRD (mean age, 47.6 years; 63.1%, female; mean follow-up, 19.7 months) had ≥ 1 co-occurring key physical condition (cardiovascular, 52.5%; metabolic, 48.2%; respiratory, 16.4%; and cancer, 9.5%). Relative to non-TRD MDD and non-MDD cohorts, respectively, patients with TRD had 46% and 235% more inpatient admissions, 28% and 128% more emergency department visits, and 53% and 155% more outpatient visits (all P < 0.05). Health care costs were $22,541 in the TRD cohort, $17,450 in the non-TRD MDD cohort, and $10,047 in the non-MDD cohort, yielding cost differences of $5,091 (vs. non-TRD MDD) and $12,494 (vs. non-MDD; all P < 0.01). In patients with work loss data available (n = 278/cohort), those with TRD had 2.0 and 2.9 times more work loss as well as $8,676 and $10,323 higher work loss costs relative to those with non-TRD MDD and without MDD, respectively (all P < 0.001).

CONCLUSIONS

In patients with physical conditions, those with TRD had higher HRU and health care costs, work loss days, and associated costs compared with non-TRD MDD and non-MDD cohorts.

DISCLOSURES

This study was sponsored by Janssen Scientific Affairs (JSA), which was involved in all aspects of the research, including the design of the study; the collection, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication. Joshi and Daly are employed by JSA. Zhdanava, Pilon, Rossi, Morrison, and Lefebvre are employees of Analysis Group, which received funding from JSA for conducting this study and has received consulting fees from Novartis Pharmaceuticals and GSK, unrelated to this study. Kuvadia is employed by Integrated Resources, which has provided research services to JSA unrelated to this study; Joshi reports past employment by and stock ownership in Johnson & Johnson; Nelson reports advisory board, data and safety monitoring board, and consulting fees from Assurex, Eisai, FSV-7, JSA, Lundbeck, Otsuka, and Sunovion and royalties from UpToDate, unrelated to this study. This work was presented at AMCP Nexus 2019, held in National Harbor, MD, from October 29 to November 1, 2019.