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Record W2142083843 · doi:10.1186/1471-2377-12-76

Direct cost associated with acquired brain injury in Ontario

2012· article· en· W2142083843 on OpenAlexafffundabout
Amy Chen, Ksenia Bushmeneva, Brandon Zagorski, Angela Colantonio, Daria Parsons, Walter P. Wodchis

Bibliographic record

VenueBMC Neurology · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoToronto Rehabilitation Institute
FundersToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term CareOntario Neurotrauma Foundation
KeywordsNeurologyMedicineNeurosurgeryNeurochemistryTraumatic brain injuryPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Acquired Brain Injury (ABI) from traumatic and non traumatic causes is a leading cause of disability worldwide yet there is limited research summarizing the health system economic burden associated with ABI. The objective of this study was to determine the direct cost of publicly funded health care services from the initial hospitalization to three years post-injury for individuals with traumatic (TBI) and non-traumatic brain injury (nTBI) in Ontario Canada. METHODS: A population-based cohort of patients discharged from acute hospital with an ABI code in any diagnosis position in 2004 through 2007 in Ontario was identified from administrative data. Publicly funded health care utilization was obtained from several Ontario administrative healthcare databases. Patients were stratified according to traumatic and non-traumatic causes of brain injury and whether or not they were discharged to an inpatient rehabilitation center. Health system costs were calculated across a continuum of institutional and community settings for up to three years after initial discharge. The continuum of settings included acute care emergency departments inpatient rehabilitation (IR) complex continuing care home care services and physician visits. All costs were calculated retrospectively assuming the government payer's perspective. RESULTS: Direct medical costs in an ABI population are substantial with mean cost in the first year post-injury per TBI and nTBI patient being $32132 and $38018 respectively. Among both TBI and nTBI patients those discharged to IR had significantly higher treatment costs than those not discharged to IR across all institutional and community settings. This tendency remained during the entire three-year follow-up period. Annual medical costs of patients hospitalized with a brain injury in Ontario in the first follow-up year were approximately $120.7 million for TBI and $368.7 million for nTBI. Acute care cost accounted for 46-65% of the total treatment cost in the first year overwhelming all other cost components. CONCLUSIONS: The main finding of this study is that direct medical costs in ABI population are substantial and vary considerably by the injury cause. Although most expenses occur in the first follow-up year ABI patients continue to use variety of medical services in the second and third year with emphasis shifting over time from acute care and inpatient rehabilitation towards homecare physician services and long-term institutional care. More research is needed to capture economic costs for ABI patients not admitted to acute care.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.334
Teacher spread0.242 · 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

Citations120
Published2012
Admission routes3
Has abstractyes

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