Direct and Indirect Cost Burden and Change of Employment Status in Treatment-Resistant Depression
Bibliographic record
Abstract
BACKGROUND: Treatment-resistant depression (TRD) poses a substantial burden to health care payers including employers, costing an estimated $29 billion-$48 billion yearly in the United States. Furthermore, variation of burden across increasing levels of resistance and the potential impact of TRD on employment status remain largely unexplored. OBJECTIVE: To evaluate health care resource utilization (HRU) and costs, work loss, indirect costs, and employment status change in TRD. METHODS: A claims-based algorithm identified adults with TRD from a US claims database of privately insured employees and dependents (January 2010-March 2015). TRD patients were matched 1:1 on demographics to patients with major depressive disorder (MDD) (non-TRD MDD) and without MDD (non-MDD), who were identified using ICD-9-CM codes. Costs, HRU, and employment status change were compared over 2 years following the first antidepressant (randomly imputed date for non-MDD), adjusting for baseline comorbidity index and costs. RESULTS: TRD patients (N = 6,411) had more HRU than either matched control cohort, translating into higher per patient per year (PPPY) health care costs: $6,709 and $9,917 more than non-TRD MDD and non-MDD patients, respectively (P < .001 for both). TRD patients with work loss data (N = 1,908) had 35.8 work loss days PPPY (1.7 and 6.2 times the work loss rate in non-TRD MDD and non-MDD patients, respectively). Work loss-related costs in TRD patients were $1,811 higher than non-TRD MDD and $3,460 higher than in non-MDD patients (P < .001). TRD patients had 1.3-1.4 times the rate of employment status change versus control cohorts (all P < .05). CONCLUSIONS: TRD, even compared to MDD, poses a significant direct and indirect cost burden to US employers and may be associated with higher rates of employment status change.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 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".