US Integrated Delivery Networks Perspective on Economic Burden of Patients with Treatment-Resistant Depression: A Retrospective Matched-Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Our objective was to assess healthcare resource utilization (HRU) and costs among patients with major depressive disorder (MDD) with and without treatment-resistant depression (TRD) and those without MDD in US Integrated Delivery Networks (IDNs). METHODS: This was a retrospective matched-cohort study. The Optum© Integrated Claims Electronic Health Record de-identified database was used to identify adult patients with TRD (January 2011-June 2017) across US IDNs. TRD patients were propensity score matched 1:1 with non-TRD MDD and non-MDD patients on demographics. Rates of HRU and costs were compared up to 2 years following the first antidepressant pharmacy claim (or randomly imputed date for non-MDD patients) using negative binomial and ordinary least squares regressions, respectively, with 95% confidence intervals (CIs) from nonparametric bootstraps (costs only) adjusted for baseline comorbidity index and costs. RESULTS: All 1582 TRD patients were matched to non-TRD MDD and non-MDD patients and evaluated. TRD patients were on average 46 years old, and 67% were female. Mean duration of observation was 20.1, 19.6, and 17.9 months in the TRD, non-TRD MDD, and non-MDD cohorts, respectively. Patients with TRD had significantly higher rates of HRU than did non-TRD MDD patients (inpatient visits 0.35 vs. 0.16 per patient per year [PPPY]; adjusted incidence rate ratio [IRR] 2.04 [95% CI 1.74-2.39]) and non-MDD patients (0.35 vs. 0.09 PPPY, adjusted IRR 3.05 [95% CI 2.54-3.66]). TRD patients incurred significantly higher costs PPPY than did non-TRD MDD patients ($US25,807 vs. 13,701, adjusted cost difference $US9479 [95% CI 7071-11,621]) and non-MDD patients ($US25,807 vs. 8500, adjusted cost difference $US11,433 [95% CI 8668-13,876]). CONCLUSIONS: HRU and costs associated with TRD are significant in US IDNs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".