The Economic Burden of Schizophrenia in the United States in 2013
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to estimate the US societal economic burden of schizophrenia and update the 2002 reported costs of $62.7 billion given the disease management and health care structural changes of the last decade. METHODS: A prevalence-based approach was used to assess direct health care costs, direct non-health care costs, and indirect costs associated with schizophrenia (ICD-9 codes 295.xx) for 2013, with cost adjustments where necessary. Direct health care costs were estimated using a retrospective matched cohort design using the Truven Health Analytics MarketScan Commercial Claims and Encounters, Medicare Supplemental, and Medicaid Multistate databases. Direct non-health care costs were estimated for law enforcement, homeless shelters, and research and training. Indirect costs were estimated for productivity loss from unemployment, reduced work productivity among the employed, premature mortality (ie, suicide), and caregiving. RESULTS: The economic burden of schizophrenia was estimated at $155.7 billion ($134.4 billion-$174.3 billion based on sensitivity analyses) for 2013 and included excess direct health care costs of $37.7 billion (24%), direct non-health care costs of $9.3 billion (6%), and indirect costs of $117.3 billion (76%) compared to individuals without schizophrenia. The largest components were excess costs associated with unemployment (38%), productivity loss due to caregiving (34%), and direct health care costs (24%). CONCLUSIONS: Schizophrenia is associated with a significant economic burden where, in addition to direct health care costs, indirect and non-health care costs are strong contributors, suggesting that therapies should aim at improving not only symptom control but also cognition and functional performance, which are associated with substantial non-health care and indirect costs.
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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 | 0.002 |
| 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 | 0.000 |
| 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".