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Enregistrement W2030754292 · doi:10.1097/bot.0000000000000099

Status of Road Safety and Injury Burden

2014· article· en· W2030754292 sur OpenAlexaff
Paul J. Moroz, Bruce D. Browner

Notice bibliographique

RevueJournal of Orthopaedic Trauma · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueTrauma and Emergency Care Studies
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésMedicineOccupational safety and healthInjury preventionPoison controlMedical emergencyPathology

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: INCIDENCE AND DISEASE BURDEN Everyday on the roads of the world, more than 3500 people die and 30,000 are severely injured,1 and many of the injured develop long-term disabilities, which drain already low-resourced health care systems.2 Most of the death and disability—85% of deaths and 90% of disability adjusted life years—occur in the low-to-middle income countries (LMIC) of the world with pedestrians, cyclists, and mass transport riders being a significant portion.3 The African region, especially sub-Saharan Africa (SSA), faces the highest rate of surgical disability adjusted life years in the World, led primarily by motor vehicle–related injuries,4 with rates estimated to worsen over time.5 Although Africa shows one of the lowest rates of registered vehicles per person in the world, the fatality rate from crashes in Africa significantly exceeds high-income motorized regions.6,7 Most posttraumatic disabilities are due to injuries to the extremities or the spine,8,9 with complex long-bone injuries among the most disabling, even in high-income trauma systems.10 In Africa, where solid trauma data are limited, what studies that do exist suggest that 78% of injury-based disabilities come from complex extremity injuries.8,11 COSTS AND ECONOMIC IMPACT OF ROAD TRAUMA Globally, the annual burden of road crash costs is estimated at US $518 billion, with approximately US $65 to 100 billion coming from LMIC, exceeding the total annual amount that LMIC countries receive in annual development assistance.12,13 Although road crashes cost High Income Country roughly 2% of their Gross National Product, World Bank estimates of costs to African countries range generally between 1% and 3% of their Gross National Product each year, and as high as 5% in Kenya14 and Malawi, and as high as 9% in Angola.13 World Health Organization (WHO) projections estimate the economic costs of road crashes to increase by 80% over the next decade. PRESSING PROBLEMS The greatest impact on road traffic crashes in Africa will be the efforts to improve road enforced traffic laws and safety behavior, similar to the experience in North America over the past 5 decades. Seat belt and helmets laws, as well as speed reduction and impaired driving legislation have suffered from weak policing in the majority of Africa. There has been no overall improvement in the number of countries in Africa having comprehensive road safety legislation over the past decade.13 Although trauma surgeons can effectively advocate for better safety behavior and legislative efforts, their real task is to improve prehospital and inhospital trauma. However, the rapid growth of motorization in Africa has not been accompanied with improvements in health care, especially emergency health care. Prehospital emergency care in SSA is virtually nonexistent. Recent WHO estimates found Africa to have the least trained nurses and doctors in emergency care compared with other WHO regions.13 Reliable data on road injuries in Africa are scarce and unreliable.13 What does exist suggests a disturbing trend of actual worsening of fatality rates.13–15 Country-level data on the injury burden in SSA are particularly limited,16 with 77% of countries fatality data coming exclusively from police records, a source known to be variable in quality.17 Only 16% of countries in Africa reported the use of combined data from police and health facility records.13 FUTURE OUTLOOKS AND INITIATIVES It is now nearly half way into the United Nations Decade of Action for Road Safety 2011–2020. African orthopaedic surgeons are trained and starting to advocate for road traffic care on their continent, but Africa is the most deficient region in the world for orthopaedic surgeons.8,11,14 “Organized surgical institutions” (trauma associations, surgical NGO's, University departments of surgery, and so on) in the developed world can help to nurture effective trauma surveillance programs to develop cost-effective, outcome-oriented, region-specific surgical interventions there. Such trauma registries have been shown to work in SSA, but they lack infrastructure and sustainable funding.10,13,18,19 Organized surgical institutions and individual High Income Country orthopaedic trauma surgeons can and are promoting surgical training programs, fellowship training programs, and trauma training opportunities, but more needs to be performed.20 Organized aid institutions, such as the WHO's Global Initiative for Essential and Emergency Surgical Care (GIEESC—see other article in this edition of the journal on GIEESC), can provide needs assessment tools to build surgical capacity in district hospitals to improve trauma care in Africa.21,22 Partnering with emerging African trauma institutions create opportunities for collaboration not only in one of the world's poorest regions but also with one of the world's major emerging economies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,155

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0460,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,287
Écart entre enseignants0,271 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2014
Routes d'admission1
Résumé présentoui

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