“Current incidence of injuries in Iran; findings of STEPS survey 2021”
Bibliographic record
Abstract
Background: The updated epidemiology of injuries at the national and sub-national levels are required for policymakers to effectively handle the burden of injuries. This paper aimed to assess the incidence and risk factors of different injuries in Iran based on a recent national survey. Methods: We used data from Iran Stepwise approach to surveillance (STEPS) Survey 2021, a population-based study in urban and rural areas of Iran's 31 provinces. A multistage clustered probability design and weighting adjustments were used to select eligible individuals and generate estimations. We estimated the incidence of injuries, assessed sociodemographic variables, and identified potential behavioral risk factors associated with injuries, and results were reported for sociodemographic and geographic stratifications. Result: Data from 27,874 participants of the STEPS survey were assessed, of which 1538 (5.5 %, 95 % CI: [5.2-5.8]) reported having an injury in the past 12 months. Falls (44.4 %) were the most common cause of injury, followed by road traffic injury (21.7 %) and exposure to mechanical forces (16.5 %). Except for falls and burns, males had a higher proportion of all types of injuries. Logistic regression analysis showed that being male (OR: 1.7, [1.5, 2.0]) and being an occasional or heavy alcohol drinker (OR: 2.0, [1.3, 3.0] and OR: 2.7, [1.7, 4.1] respectively) were significant risk factors associated with road traffic injuries. Seatbelt use was 90.0 % among both drivers and front-seat passengers, while the use of safety car seats for children was as low as 9.4 %. Injury incidence varied significantly among provinces, with the highest incidence among males observed in Razavi Khorasan (11.2 %) and among females observed in Tehran (12.0 %). Conclusion: This study investigated the updated epidemiology of injuries in Iran and revealed socioeconomic and geographic disparities across country. This epidemiological information can be used to modify injury prevention programs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".