Global prevalence and incidence of traumatic spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) is a traumatic event that impacts a patient's physical, psychological, and social well-being and places substantial financial burden on health care systems. To determine the true impact of SCI, this systematic review aims to summarize literature reporting on either the incidence or prevalence of SCI. METHODS: A systematic search was conducted using PubMed, MEDLINE, MEDLINE in process, EMBASE, Cochrane Controlled Trial Register, and Cochrane Database of Systematic Reviews to identify relevant literature published through June 2013. We sought studies that provided regional, provincial/state, or national data on the incidence of SCI or reported estimates of disease prevalence. The level of evidence of each study was rated using a scale that evaluated study design, methodology, sampling bias, and precision of estimates. RESULTS: The initial search yielded 5,874 articles, 48 of which met the inclusion criteria. Forty-four studies estimated the incidence of SCI and nine reported the prevalence, with five discussing both. Of the incidence studies, 14 provided figures at a regional, ten at a state or provincial level and 21 at a national level. The prevalence of SCI was highest in the United States of America (906 per million) and lowest in the Rhone-Alpes region, France (250 per million) and Helsinki, Finland (280 per million). With respect to states and provinces in North America, the crude annual incidence of SCI was highest in Alaska (83 per million) and Mississippi (77 per million) and lowest in Alabama (29.4 per million), despite a large percentage of violence injuries (21.2%). Annual incidences were above 50 per million in the Hualien County in Taiwan (56.1 per million), the central Portugal region (58 per million), and Olmsted County in Minnesota (54.8 per million) and were lower than 20 per million in Taipei, Taiwan (14.6 per million), the Rhone-Alpes region in France (12.7 per million), Aragon, Spain (12.1 per million), Southeast Turkey (16.9 per million), and Stockholm, Sweden (19.5 per million). The highest national incidence was 49.1 per million in New Zealand, and the lowest incidences were in Fiji (10.0 per million) and Spain (8.0 per million). The majority of studies showed a high male-to-female ratio and an age of peak incidence of younger than 30 years old. Traffic accidents were typically the most common cause of SCI, followed by falls in the elderly population. CONCLUSION: This review demonstrates that the incidence, prevalence, and causation of SCI differs between developing and developed countries and suggests that management and preventative strategies need to be tailored to regional trends. The rising aging population in westernized countries also indicates that traumatic SCI secondary to falls may become an increasing public health challenge and that incidence among the elderly may rise with increasing life expectancy.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".