The Financial Burden of Road Traffic Injuries in Mozambique: A Hospital‐Related Cost‐of‐Illness Study of Maputo Central Hospital
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries (RTIs) are increasingly being recognized for their significant economic impact. Mozambique, like other low-income countries, suffers staggering rates of road traffic collisions. To our knowledge, this is the first study to estimate direct hospital costs of RTIs using a bottom-up, micro-costing approach in the Mozambican context. This study aims to calculate the direct, inpatient costs of RTIs in Mozambique and compare it to the financial capacity of the Mozambican public health care system. METHODS: This was a retrospective, single-centre study. Charts of all patients with RTIs admitted to Maputo Central Hospital over a period of 2 months were reviewed. The costs were recorded and analysed based on direct costs, human resource costs, and overhead costs. Costs were calculated using a micro-costing approach. RESULTS: In total, 114 patients were admitted and treated for RTIs at Maputo Central Hospital during June-July 2015. On average, the hospital cost per patient was US$ 604.28 (IQR 1033.58). Of this, 44% was related to procedural costs, 23% to diagnostic imaging costs, 17% to length-of-stay costs, 9% to medication costs, and 7% to laboratory test costs. The average annual inpatient cost of RTIs in Mozambique was almost US$ 116 million (0.8% of GDP). CONCLUSION: The financial burden of RTIs in Mozambique represents approximately 40% of the annual public health care budget. These results help highlight the economic impact of trauma in Mozambique and the importance of an organized trauma system to reduce such costs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".