The Economic Burden of Posttraumatic Stress Disorder in the United States From a Societal Perspective
Bibliographic record
Abstract
To estimate the economic burden of posttraumatic stress disorder (PTSD) in the United States civilian and military populations from a societal perspective. A prevalence-based and human capital approach was used to estimate the total excess costs of PTSD in 2018 from insurance claims data, academic literature, and governmental publications. Excess direct health care costs (pharmacy, medical), direct non-health care costs (research and training, substance use, psychotherapy, homelessness, disability), and indirect costs (unemployment, productivity loss, caregiving, premature mortality) associated with PTSD were compared between adults with PTSD and adults without PTSD, or the general population if information was not available for adults without PTSD. The total excess economic burden of PTSD in the US was estimated at $232.2 billion for 2018 ($19,630 per individual with PTSD). Total excess costs were $189.5 billion (81.6%) in the civilian population and $42.7 billion (18.4%) in the military population, corresponding to $18,640 and $25,684 per individual with PTSD in the civilian and military populations, respectively. In the civilian population, the excess burden was driven by direct health care ($66.0 billion) and unemployment ($42.7 billion) costs. In the military population, the excess burden was driven by disability ($17.8 billion) and direct health care ($10.1 billion) costs. The economic burden of PTSD goes beyond direct health care costs and has been found to rival costs for other costly mental health conditions. Increased awareness of PTSD, development of more effective therapies, and expansion of evidence-based interventions may be warranted to reduce the large clinical and economic burden of PTSD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".