Disease and Non-Battle Injury in Deployed Military: A Systematic Review and Meta-analysis
Notice bibliographique
Résumé
INTRODUCTION: Disease and non-battle injury (DNBI) has historically been the leading casualty type among service members in warfare and a leading health problem confronting military personnel, resulting in significant loss of manpower. Studies show a significant increase in disease burden for DNBI when compared to combat-related injuries. Understanding the causes of and trends in DNBI may help guide efforts to develop preventive measures and help increase medical readiness and resiliency. However, despite its significant disease burden within the military population, DNBI remains less studied than battle injury. In this review, we aimed to evaluate the recently published literature on DNBI and to describe the characteristics of these recently published studies. MATERIALS AND METHODS: This systematic review is reported in the Prospective Register of Systematic Reviews database. The systematic search for published articles was conducted through July 21, 2022, in Cumulative Index of Nursing and Allied Health, Cochrane Library, Defense Technical Information Center, Embase, and PubMed. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses, the investigators independently screened the reference lists on the Covidence website (covidence.org). An article was excluded if it met any of the following criteria: (1) Published not in English; (2) published before 2010; (3) data used before 2001; (4) case reports, commentaries, and editorial letters; (5) systematic reviews or narrative reviews; (6) used animal models; (7) mechanical or biomechanical studies; (8) outcome was combat injury or non-specified; (9) sample was veterans, DoD civilians, contractors, local nationals, foreign military, and others; (10) sample was U.S. Military academy; (11) sample was non-deployed; (12) bioterrorism study; (13) qualitative study. The full-text review of 2 independent investigators reached 96% overall agreement (166 of 173 articles; κ = 0.89). Disagreements were resolved by a third reviewer. Study characteristics and outcomes were extracted from each article. Risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-analysis of pooled estimates of incidence rates for disease (D), non-battle injury (NBI), and combined DNBI was created using random-effects models. RESULTS: Of the 3,401 articles, 173 were included for the full review and 29 (16.8%) met all inclusion criteria. Of the 29 studies included, 21 (72.4%) were retrospective designs, 5 (17.2%) were prospective designs, and 3 (10.3%) were surveys. Across all studies, the median number of total cases reported was 1,626 (interquartile range: 619.5-10,203). The results of meta-analyses for 8 studies with reported incidence rates (per 1,000 person-years) for D (n = 3), NBI (n = 7), and DNBI (n = 5) showed pooled incidence rates of 22.18 per 1,000 person-years for D, 19.86 per 1,000 person-years for NBI, and 50.97 per 1,000 person-years for combined DNBI. Among 3 studies with incidence rates for D, NBI, and battle injury, the incidence rates were 20.32 per 1,000 person-years for D, 6.88 per 1,000 person-years for NBI, and 6.83 per 1,000 person-years for battle injury. CONCLUSIONS: DNBI remains the leading cause of morbidity in conflicts involving the U.S. Military over the last 20 years. More research with stronger designs and consistent measurement is needed to improve medical readiness and maintain force lethality. LEVEL OF EVIDENCE: Systematic Review and Meta-Analysis, Level III.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».