COVID-19 in Vietnam and Its Impact on Road Trauma: Retrospective Study Based on National Data
Bibliographic record
Abstract
BACKGROUND: Despite significant improvement in the last decade, road trauma remains a substantial contributor to deaths in Vietnam. The COVID-19 pandemic necessitated public health measures that had an unforeseen benefit on road trauma in high-income countries. We investigate if this reduction was also seen in a low- to middle-income country like Vietnam. OBJECTIVE: Our aim was to investigate how the COVID-19 pandemic and the government policies implemented in response to it impacted road trauma fatalities in Vietnam. We also compared this impact to other government policies related to road trauma implemented in the preceding 14 years (2007-2020). METHODS: COVID-19 data were extracted from the Vietnamese Ministry of Health database. Road traffic deaths from 2007 to 2021 were derived from the Vietnamese General Statistical Office. We used Stata software (version 17; StataCorp) for statistical analysis. Poisson regression modeling was used to estimate trends in road fatality rates based on annual national mortality data for the 2007-2021 period. The actual change in road traffic mortality in 2021 was compared with calculated figures to demonstrate the effect of COVID-19 on road trauma fatalities. We also compared this impact to other government policies that aimed to reduce traffic-related fatalities from 2007 to 2020. RESULTS: Between 2007 and 2020, the number of annual road traffic deaths decreased by more than 50%, from 15.3 to 7 per 100,000 population, resulting in an average reduction of 5.4% per annum. We estimated that the road traffic mortality rate declined by 12.1% (95% CI 8.9-15.3%) in 2021 relative to this trend. The actual number of road trauma deaths fell by 16.4%. This reduction was largely seen from August to October 2021 when lockdown and social distancing measures were in force. CONCLUSIONS: In 2021, the road traffic-related death reduction in Vietnam was 3 times greater than the trend seen in the preceding 14 years. The public health response to the COVID-19 pandemic in Vietnam was associated with a third of this reduction. It can thus be concluded that government policies implemented to address the COVID-19 pandemic resulted in a 4.3% decrease in road traffic deaths in 2021. This has been observed in high-income countries, but we have demonstrated this for the first time in a low- and middle-income country.
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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 | 0.020 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".