Estimating the Burden of Disability From Road Traffic Injuries in 5 Low- and Middle-Income Countries: Protocol for a Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries (RTIs) are a leading cause of death and unintentional injuries globally. They claim 1.35 million lives and produce up to 50 million injuries each year, causing a major drain on health systems. Despite this high burden, there is a lack of robust data on the long-term consequences of RTIs, specifically the level of disability experienced by many survivors and its impact on their everyday lives. OBJECTIVE: This study aims to characterize RTIs, disability level, and related consequences affecting adult road traffic crash survivors in 5 low- and middle-income countries (LMICs). In addition, this study estimates the role of demographic and crash- and treatment-related factors in predicting adverse outcomes and disability as well as examining the disability level among patients with RTIs, likelihood of return to normal life, and the environmental factors that may influence these outcomes after discharge from the hospital. METHODS: This prospective observational study was conducted at selected hospitals in Bangladesh, Cambodia, Ethiopia, Mexico, and Zambia. The study sample included all adult patients with RTIs admitted to the hospital for at least 24 hours. Consecutive sampling was performed until the minimum required sample size of 400 was reached for each participating country. Data were collected from patients or their caregivers using a hospital-based surveillance tool administered at the participating sites as well as a telephone-based follow-up instrument administered 1, 3, and 6 months after discharge. Descriptive analysis and multivariate models will be used to estimate the contribution of a range of factors in predicting adverse outcomes, disability, and return to normal life. RESULTS: Enrollment began in June 2021 and was completed in April 2022. Follow-up data collection ended in September 2022. Data analysis is currently underway, with results expected for publication in mid-2023. Expected results include estimates of disability among patients with RTIs as well as identifying the predictors of adverse outcomes, disability, and the likelihood of return to normal life. CONCLUSIONS: Research findings will help better understand the long-term burden of disability from RTIs in the 5 LMICs and the challenges facing survivors of road traffic crashes. They will be used to inform interventions aimed at improving the health care, social, physical, and policy conditions in LMICs that can facilitate recovery and rehabilitation for patients with RTIs, reduce the burden of disability, and enhance their participation in society. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40985.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".