The Economic Burden of Adults with Major Depressive Disorder in the United States (2019)
Bibliographic record
Abstract
INTRODUCTION: Previous societal burden estimations for major depressive disorder (MDD) often fail to account for several hidden cost components. This study provides a comprehensive evaluation of societal costs for adults with MDD in the United States (USA) in 2019. The potential impact of a more effective, rapid-acting MDD therapy vs standard of care on the economic burden of MDD was estimated to illustrate the utility of such a framework in evaluating new interventions. METHODS: This study used a prevalence-based human capital approach. Incremental costs (2019 US dollars) per individual with MDD were derived from national survey inputs and published literature and included incremental healthcare costs and indirect costs. For each cost component, the societal costs were extrapolated by multiplying the per-patient costs by the number of individuals with MDD. The impact of a more effective, rapid-acting novel therapy on the economic burden of MDD was then simulated on the basis of these inputs. RESULTS: In 2019, the number of adults with MDD in the USA was estimated at 19.8 million (62.7% female; 32.9% severe MDD), and the incremental societal economic burden of MDD was estimated at $333.7 billion ($382.4 billion in 2023 US dollars), or $16,854 per adult with MDD. The primary cost drivers were healthcare costs ($127.3 billion; 38.1%), household-related costs ($80.1 billion; 24.0%), presenteeism ($43.3 billion; 13.0%), and absenteeism ($38.4 billion; 11.5%). In the simulated scenario, a hypothetical novel therapy with a 50.0% early response rate was associated with a 7.7% reduction in the economic burden of MDD relative to standard of care over 12 months. CONCLUSIONS: The economic burden of MDD is substantial and extends beyond healthcare costs, underscoring the impact of MDD across multiple aspects of life. Such a broad societal perspective should be considered in assessing the impact of the advent of effective, rapid-acting MDD therapies.
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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 | 0.003 |
| 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) | 0.003 | 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".