The Prevalence and National Burden of Treatment-Resistant Depression and Major Depressive Disorder in the United States
Bibliographic record
Abstract
OBJECTIVE: Estimates of prevalence and burden of treatment-resistant depression (TRD) vary widely in the literature. This study evaluated the prevalence and burden of TRD and the share of TRD in the burden of medication-treated major depressive disorder (MDD) using the most commonly accepted definition of TRD and a novel bottom-up approach. METHODS: Prevalence and health care burden of TRD were estimated by synthetizing inputs across 4 similarly designed claims studies in adults covered by Medicare, Medicaid, commercial plans, and the US Veterans Health Administration (VHA). Productivity (absenteeism and presenteeism) and unemployment burden were estimated based on inputs from a study conducted with data from the Kantar National Health and Wellness Survey (NHWS; 2017). A targeted literature search for additional inputs was performed. A cost model was developed to estimate the burden of TRD and medication-treated DSM-5-defined MDD in the United States. Study outcomes were the 12-month prevalence of TRD and the annual health care, productivity, and unemployment burden of TRD and medication-treated MDD in the United States. RESULTS: The estimated 12-month prevalence of medication-treated MDD in the United States was 8.9 million adults, and 2.8 million (30.9%) had TRD. The total annual burden of medication-treated MDD among the US population was $92.7 billion, with $43.8 billion (47.2%) attributable to TRD. The share of TRD was 56.6% ($25.8 billion) of the health care burden, 47.7% ($8.7 billion) of the unemployment burden, and 32.2% ($9.3 billion) of the productivity burden of medication-treated MDD. CONCLUSIONS: TRD is associated with disproportionate health care costs and unemployment, suggesting potentially large economic and societal gains with effective management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".