Prevalence and predictors of injuries in Kenya: findings from the national STEPs survey
Bibliographic record
Abstract
BACKGROUND: Injuries are becoming an increasingly important public health challenge globally, and are responsible for 9% of deaths. Beyond their impact on health and well-being, fatal and non-fatal injuries also affect social and economic development for individuals concerned. Kenya has limited data on the magnitude and factors associated with injuries. This study sought to determine the magnitude and risk factors for injuries in Kenya and to identify where the largest burden lies. METHODS: A national population-based household survey was conducted from April-June 2015 among adults age 18-69 years. A three-stage cluster sample design was used to select clusters, households and eligible individuals based on WHO guidelines. We estimated the prevalence of injuries, identified factors associated with injuries and the use of protective devices/practices among road users. Multivariate logistic regression was used to identify potential factors associated with injuries. RESULTS: A total of 4484 adults were included in the study. Approximately 15% had injuries from the past 12 months, 60.3% were males. Four percent of the respondents had been injured in a road traffic crash, 10.9% had experienced unintentional injuries other than road traffic injuries while 3.7% had been injured in violent incidents. Among drivers and passengers 12.5% reported always using a seatbelt and 8.1% of the drivers reported driving while drunk. The leading causes of injuries other than road traffic crashes were falls (47.6%) and cuts (34.0%). Males (p = 0.001), age 18-29 (p < 0.05) and smokers (p = 0.001) were significantly more likely to be injured in a road traffic crash. A higher social economic status (p = 0.001) was protective against other unintentional injuries while students had higher odds for such types of injuries. Heavy episodic drinking (p = 0.001) and smoking (p < 0.05) were associated with increased likelihood of occurrence of a violent injury. CONCLUSIONS: Our study found that male, heavy episodic drinkers, current smokers and students were associated with various injury types. Our study findings highlight the need to scale up interventions for injury prevention for specific injury mechanisms and target groups. There is need for sustained road safety mass media campaigns and strengthened enforcement on helmet wearing, seatbelt use and drink driving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".