Disease and Non-Battle Injury in Deployed Military: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
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.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".