Injury Mortality in Vietnam: Patterns and Trends, 2005-2013
Bibliographic record
Abstract
CONTEXT: Injury is a growing public health problem worldwide. Deaths due to injuries account for 10% of the world's mortality. More than 90% of the world's injury-related deaths occur in low- and middle-income countries such as Vietnam. The public health burden has been recognized by government; however, there is a need to study and better understand the epidemiology and trends for injuries and injury-related mortality to be able to better address this burden. OBJECTIVES: In this article, we analyze the patterns and trends in injury mortality from 2005 to 2013 in Vietnam. DESIGN: All registered deaths from injuries from January 2005 to December 2013 were extracted from the Ministry of Health death recording system (A6). Mortality rates per 100 000 population per year were calculated. A linear regression model was used to estimate the injury mortality trends. RESULTS: In the 9-year period, 313 101 deaths due to injuries were recorded in the A6 system; this accounts for about 10% of all deaths. The leading causes of injury-related death were road traffic injuries among the entire population, while drowning was the leading cause of death among children. Other unintentional injuries including occupational, fall, poisoning, and exposure to electric current also accounted for a substantial proportion of the burden of injury. There is a significant reduction trend found in drowning among children 0 to 18 years of age and significant increasing trends in intentional injury causes. CONCLUSIONS: While injuries have been recognized as a public health problem in Vietnam in the last decade, as seen by our analysis, there is a need for concerted action to reduce their burden. Of particular concern is the increasing prominence of intentional injuries. Improved data systems, increasing the awareness in the community, and making appropriate policies and implementing them, as well as implementing effective, evidence-based interventions are all key to decreasing this burden. This is an important study describing injury mortality in Vietnam for the period 2005-2013. The results of the study show that injury death remains an important public health issue that needs more attention from government and relevant agencies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".