The Economic and Social Burden of Traumatic Injuries: Evidence from a Trauma Hospital in Port‐au‐Prince, Haiti
Bibliographic record
Abstract
INTRODUCTION: The cost of traumatic injury is unknown in Haiti. This study aims to examine the burden of traumatic injury of patients treated and evaluated at a trauma hospital in the capital city of Port-au-Prince. METHODS: A retrospective cross-sectional chart review study was conducted at the Hospital Bernard Mevs Project Medishare for all patients evaluated for traumatic injury from December 2015 to January 2016, as described elsewhere (Zuraik and Sampalis in World J Surg, https://doi.org/10.1007/s00268-017-4088-2 , 2017). Direct medical costs were obtained from patient hospital bills. Indirect and intangible costs were calculated using the human capital approach. RESULTS: A total of 410 patients were evaluated for traumatic injury during the study period. Total costs for all patients were $501,706 with a mean cost of $1224. Indirect costs represented 63% of all costs, direct medical costs 19%, and intangible costs 18%. Surgical costs accounted for the majority of direct medical costs (29%). Patients involved in road traffic accidents accounted for the largest number of injuries (41%) and the largest percentage of total costs (51%). Patients with gunshot wounds had the highest total mean costs ($1566). Mean costs by injury severity ranged from $62 for minor injuries, $1269 for serious injuries, to $13,675 for critical injuries. CONCLUSION: Injuries lead to a significant economic burden for individuals treated at a semi-private trauma hospital in the capital city of Port-au-Prince, Haiti. Programs aimed at reducing injuries, particularly road traffic accidents, would likely reduce the economic burden to the nation.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".