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Record W4399812970 · doi:10.1177/23814683241260423

Screening and Treatment of Posttraumatic Stress Disorder in Wildfire Evacuees: A Cost-Utility Analysis

2024· article· en· W4399812970 on OpenAlexafffundabout
Michael Lebenbaum, Syed Ahmed Hassan

Bibliographic record

VenueMDM Policy & Practice · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsParoxetineWillingness to payQuality-adjusted life yearMedicineCost–utility analysisEconomic evaluationPosttraumatic stressIntervention (counseling)PsychologyPsychiatryClinical psychologyCost effectivenessEconomicsAntidepressant

Abstract

fetched live from OpenAlex

Background. Global climate change is resulting in dramatic increases in wildfires. Individuals exposed to wildfires experience a high burden of posttraumatic stress disorder (PTSD), and the cost-effectiveness of the treatment options to address PTSD from wildfires has not been studied. The objective of this study was to conduct a cost-utility analysis comparing screening followed by treatment with paroxetine or trauma-focused cognitive behavioral therapy (TF-CBT) versus no screening in Canadian adult wildfire evacuees. Methods. Using a Markov model, quality-adjusted life-years (QALYs) and costs were evaluated over a 5-y time horizon using health care and societal perspectives. All costs and utilities in the model were discounted at 1.5%. Probabilistic and deterministic sensitivity analyses examined the uncertainty in the incremental net monetary benefit (INMB) under a willingness-to-pay threshold of $50,000. Results. From a societal perspective, no screening (NMB = $177,641) was dominated by screening followed by treatment with paroxetine (NMB = $180,733) and TF-CBT (NMB = $181,787), with TF-CBT having the highest likelihood of being cost-effective at a willingness-to-pay threshold of $50,000 per QALY (probability = 0.649). The initial prevalence of PTSD, probability of acceptance of treatment, and costs of productivity had the largest impact on the INMB of both paroxetine or TF-CBT versus no screening. Neither intervention was cost-effective at a willingness-to-pay threshold of $50,000 per QALY from a health care perspective. Interpretation. Screening followed by treatment with paroxetine or TF-CBT compared with no screening was found to be cost-saving while providing additional QALYs in wildfire evacuees. Governments should consider funding screening programs for PTSD followed by treatment with TF-CBT for wildfire evacuees. Highlights Two prior studies examined the cost-effectiveness of screening followed by treatment for PTSD among individuals exposed to other disaster-type events (i.e., terrorist attack and Hurricane Sandy) and found screening followed by treatment (i.e., cognitive behavioral therapy [CBT]) to be highly cost-effective. Among wildfire evacuees, screening followed by treatment with paroxetine or trauma-focused (TF)–CBT provides additional quality-adjusted life-years (QALYs) and is cost-saving from a societal perspective. TF-CBT was the treatment option found most likely to be cost-effective. Neither treatment option was cost-effective at a willingness-to-pay threshold of $50,000 per QALY from a health care perspective. Screening programs for PTSD should be considered for wildfire evacuees, and individuals diagnosed with PTSD could be prescribed either TF-CBT or paroxetine depending on their preference and resources availability.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.087
GPT teacher head0.417
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes3
Has abstractyes

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