Economic burden of road traffic injuries in sub-Saharan Africa: a systematic review of existing literature
Bibliographic record
Abstract
OBJECTIVE: This systematic review aims to explore and synthesise existing literature on the direct and indirect costs from road traffic injuries (RTIs) in sub-Saharan Africa (SSA), the quality of existing evidence, methods used to estimate and report these costs, and the factors that drive the costs. METHODOLOGY: MEDLINE, SCOPUS, ProQuest Central, Web of Science, Global Index Medicus, Embase, World Bank Group e-Library, Econlit, Google Scholar and WHO webpages were searched for relevant literature. References of selected papers were also examined for related articles. Screening was done following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were included in this review if they were published by March 2019, written in English, conducted in SSA and reported original findings on the cost of illness or economic burden of RTIs. The results were systematically examined, and the quality assessed by two reviewers using a modified Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. RESULTS: Eleven studies met the inclusion criteria. RTIs can cost between INT$119 and 178 634 per injury and INT$486 and 12 845 per hospitalisation. Findings show variability in costing methods and inadequacies in the quality of existing evidence. Prolonged hospital stays, surgical sundries and severity of injury were the most common factors associated with cost. CONCLUSION: While available data are limited, evidence shows that the economic burden of RTIs in SSA is high. Poor quality of existing evidence and heterogeneity in costing methods limit the generalisability of costs reported.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".