Trends in Cause-Specific Injury Mortality in China in 2005-2019: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Over the last few decades, although the age-standardized mortality rate (ASMR) of injury has shown a significant declining trend in China, this pattern has dramatically reversed recently. OBJECTIVE: We aimed to elucidate the geographical, demographic, and temporal trends of cause-specific injuries, the reversal phenomenon of these trends, and the fluctuations of injury burden from 2005 to 2019 in China. METHODS: A longitudinal observational study was performed using the raw data of injury deaths in the National Cause-of-Death surveillance data provided by the disease surveillance points system in 2005-2019. The cause-specific injuries were divided into disparate subgroups by sex, age, urban/rural region, and eastern/central/western areas of China. The burden of injury was assessed using potential years of life lost (PYLL), average years of life lost (AYLL), and PYLL rate (PYLLR). Temporal trends of mortality rates and burden were evaluated using best-fitting joinpoint models. RESULTS: Injury deaths accounted for 7.51% (1,156,504/15,403,835) of all-cause deaths in China in 2005-2019. The crude mortality rate of all-cause injury was 47.74 per 100,000 persons. The top 3 injury types (traffic accident, falls, and suicide) accounted for 70.57% (816,145/1,156,504) of all injury-related deaths. The ASMR of all-cause injury decreased (P=.003), while the crude mortality rate remained unchanged (P=.52) during 2005-2019. A significant reverse trend in ASMR of all-cause injury was observed in urban older adults since 2013, mainly due to the inverted trend in injuries from falls. A reverse trend in ASMR of suicide was observed among individuals aged 10-24 years, with notable increases by 35.18% (annual percentage change 15.4%, 95% CI 4.1%-28.0%) in men since 2017. The AYLL and PYLLR of all-cause injury among older adults showed consistent ascending trends from 2005 to 2019 (average annual percentage change [AAPC] 6.1%, 95% CI 5.4%-6.9%, 129.04% increase for AYLL; AAPC 5.4%, 95% CI 2.4%-8.4%, 105.52% increase for PYLLR). The AYLL due to suicide for individuals aged 10-24 years showed a considerable upswing tendency (AAPC 0.5%, 95% CI 0.4%-0.7%, 8.02% increase). CONCLUSIONS: Although the ASMR of all-cause injury decreased in China from 2005 to 2019, the trend in suicide among adolescents and young adults and falls among older adults has been on the rise in recent years. Interventions should be encouraged to mitigate the cause-specific burdens of injury death.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".