Estimated incidence and case fatality rate of traumatic brain injury among children (0–18 years) in Sub-Saharan Africa. A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Studies from Sub-Saharan Africa (SSA) countries have reported on the incidence and case fatality rate of children with Traumatic Brain Injury (TBI). However, there is lack of a general epidemiologic description of the phenomenon in this sub-region underpinning the need for an accurate and reliable estimate of incidence and outcome of children (0-18 years) with TBI. This study therefore, extensively reviewed data to reliably estimate incidence, case fatality rate of children with TBI and its mechanism of injury in SSA. METHODS: Electronic databases were systematically searched in English via Medline (PubMed), Google Scholar, and Africa Journal Online (AJOL). Two independent authors performed an initial screening of studies based on the details found in their titles and abstracts. Studies were assessed for quality/risk of bias using the modified Newcastle-Ottawa Scale (NOS). The pooled case fatality rate and incidence were estimated using DerSimonian and Laird random-effects model (REM). A sub-group and sensitivity analyses were performed. Publication bias was checked by the funnel plot and Egger's test. Furthermore, trim and fill analysis was used to adjust for publication bias using Duval and Tweedie's method. RESULTS: Thirteen (13) hospital-based articles involving a total of 40685 participants met the inclusion criteria. The pooled case fatality rate for all the included studies in SSA was 8.0%; [95% CI: 3.0%-13.0%], and the approximate case fatality rate was adjusted to 8.2%, [95% CI:3.4%-13.0%], after the trim-and-fill analysis was used to correct for publication bias. A sub-group analysis of sub-region revealed that case fatality rate was 8% [95% CI: 2.0%-13.0%] in East Africa, 1.0% [95% CI: 0.1% -3.0%] in Southern Africa and 18.0% [95% CI: 6.0%-29.0%] in west Africa. The pooled incidence proportion of TBI was 18% [95% CI: 2.0%-33.0%]. The current review showed that Road Traffic Accident (RTA) was the predominant cause of children's TBI in SSA. It ranged from 19.1% in South Africa to 79.1% in Togo. CONCLUSION: TBI affects 18% of children aged 0 to 18 years, with almost one-tenth dying in SSA. The most common causes of TBI among this population in SSA were RTA and falls. TBI incidence and case fatality rate of people aged 0-18 years could be significantly reduced if novel policies focusing on reducing RTA and falls are introduced and implemented in SSA.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".