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Record W4385415391 · doi:10.18553/jmcp.2023.29.8.896

Treatment patterns, health care resource utilization, and costs associated with use of atypical antipsychotics as first vs subsequent adjunctive treatment in major depressive disorder

2023· article· en· W4385415391 on OpenAlexaff
Rakesh K. Jain, François Laliberté, Guillaume Germain, Malena Mahendran, Amanda Harrington, Mousam Parikh

Bibliographic record

VenueJournal of Managed Care & Specialty Pharmacy · 2023
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineMajor depressive disorderAdjunctive treatmentPsychiatryResource useBipolar disorderSchizophrenia (object-oriented programming)MEDLINEHealth careMood

Abstract

fetched live from OpenAlex

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent mental health condition associated with substantial economic burden. Inadequate response to first-line antidepressant monotherapy is common, with most patients requiring 1 or more changes in their treatment regimen. Adjunctive treatment with atypical antipsychotics (AAs) is a guideline-recommended treatment option in patients with inadequate response. However, patients often cycle through multiple treatments before receiving adjunctive AAs, and the economic impact of this delay is unknown. OBJECTIVE: To describe adjunctive treatment patterns among patients with MDD and compare health care resource utilization (HCRU) and costs between patients whose first adjunctive therapy included an AA and those who received an AA after other adjunctive treatments. METHODS: The Merative MarketScan Commercial Database (January 1, 2014, to June 30, 2019) was used to identify patients with administrative claims meeting the following inclusion criteria: adults with newly diagnosed MDD (first observed MDD diagnosis = index diagnosis date); continuous health insurance for at least 6 months pre-index and at least 3 months post-index; and initiation of MDD treatment within 60 days post-index. Lines of therapy (LOTs), HCRU, and costs were analyzed in patients who received AA adjunctive therapy, including those who initiated AAs as the first adjunctive treatment and those who initiated AAs as subsequent adjunctive treatment. RESULTS: Of 508,830 patients meeting inclusion criteria, 121,060 (24%) received adjunctive treatment and 20,797 (4%) received an AA as adjunctive therapy. Mean time to adjunctive therapy initiation was approximately 7.3 months for AA adjunctive therapy. Patients who initiated an AA as their first adjunctive therapy compared with patients who initiated an AA as their subsequent adjunctive therapy had fewer LOTs on average (0.9 LOTs vs 3.9 LOTs) and shorter time between index diagnosis date and initiation of an AA (5 months vs 12 months). Subsequent AA initiators had significantly greater HCRU than first AA initiators (driven primarily by outpatient visits) and incurred significantly higher total health care costs, with mean all-cause and mental health–related health care cost differences per patient per year of $2,441 and $1,762, respectively (both P < 0.05). CONCLUSIONS: Less than 5% of patients in this study received an adjunctive AA as part of their MDD treatment regimen, suggesting underutilization of this recommended therapeutic approach. Patients who received an AA as their first adjunctive treatment regimen had lower HCRU and health care costs than subsequent AA initiators. Along with published evidence of clinical benefits, this potential impact on economic burden should be considered when making treatment choices for patients with inadequate response to antidepressants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.341
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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