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Record W2988380919 · doi:10.1080/13696998.2019.1686311

Economic burden associated with inadequate antidepressant medication management among patients with depression and known cardiovascular diseases: insights from a United States–based retrospective claims database analysis

2019· article· en· W2988380919 on OpenAlexaff
Sripal Bangalore, Ruchit Shah, Xin Gao, Elizabeth Pappadopulos, Chinmay Deshpande, Ahmed Shelbaya, Rita Prieto, Jennifer Stephens, Richard Chambers, Patricia Schepman, Roger S. McIntyre

Bibliographic record

VenueJournal of Medical Economics · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of TorontoUniversity Health NetworkBrain and Cognition Discovery Foundation
Fundersnot available
KeywordsMedicineMajor depressive disorderRetrospective cohort studyDepression (economics)Emergency medicineFluoxetineAntidepressantMyocardial infarctionEmergency departmentInternal medicineDatabasePsychiatryAnxiety

Abstract

fetched live from OpenAlex

Aims: The current study examined the association between insufficient major depressive disorder (MDD) care and healthcare resource use (HCRU) and costs among patients with prior myocardial infarction (MI) or stroke.Methods: This was a retrospective study conducted using the MarketScan Claims Database (2010–2015). The date of the first MI/stroke diagnosis was defined as the cardiovascular disease (CVD) index date and the first date of a subsequent MDD diagnosis was the index MDD date. Adequacy of MDD care was assessed during the 90 days following the index MDD date (profiling period) using 2 measures: dosage adequacy (average fluoxetine equivalent dose of ≥20 mg/day for nonelderly and ≥10 mg/day for elderly patients) and duration adequacy (measured as the proportion of days covered of 80% or higher for all MDD drugs). Study outcomes included all-cause and CVD-related HCRU and costs which were determined from the end of the profiling period until the end of study follow-up. Propensity-score adjusted generalized linear models (GLMs) were used to compare patients receiving adequate versus inadequate MDD care in terms of study outcomes.Results: Of 1,568 CVD patients who were treated for MDD, 937 (59.8%) were categorized as receiving inadequate MDD care. Results from the GLMs suggested that patients receiving inadequate MDD care had 14% more all-cause hospitalizations, 4% more all-cause outpatient visits, 17% more CVD-related outpatient visits, 13% more CVD-related emergency room (ER) visits, higher per patient per year CVD-related hospitalization costs ($21,485 vs. $17,756), higher all-cause outpatient costs ($2,820 vs. $2,055), and higher CVD-related outpatient costs ($520 vs. $434) compared to patients receiving adequate MDD care.Limitations: Clinical information such as depression severity and frailty, which are potential predictors of adverse CVD outcomes, could not be ascertained using administrative claims data.Conclusions: Among post-MI and post-stroke patients, inadequate MDD care was associated with a significantly higher economic burden.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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 teacher head, 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

Citations14
Published2019
Admission routes1
Has abstractyes

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