Impact of Acute Respiratory Infections on Medical Absenteeism Among Military Personnel: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Acute respiratory infections (ARI) are a significant challenge in military settings due to close communal living, which facilitates the rapid transmission of pathogens. A variety of respiratory pathogens contribute to ARI, each varying in prevalence, severity, and impact on organizational productivity. Understanding and mitigating the impact of ARI is critical for optimizing the health of military personnel and maintaining organizational productivity. Objective: This retrospective study of surveillance data aims to identify pathogens causing ARI among servicemen and determine which pathogens contribute most to medical absenteeism, defined as the combined duration of the issued medical certificate and light duty. Methods: From September 2023 to August 2024, anonymous nasopharyngeal swabs (BioFire FilmArray Respiratory Panel) were collected from Singapore Armed Forces servicemen presenting with ARI symptoms after a doctor's consultation at a local military camp's medical centre. The presence of fever and duration of medical certificate and light duty were self-reported by Singapore Armed Forces servicemen. Results: A total of 1095 nasopharyngeal swabs were collected, of which 608 (55.5%) tested positive. The most common respiratory pathogen was human rhinovirus/enterovirus (HRV/HEV) in 303 (27.7%) individuals. The highest proportions of fever were observed in servicemen with influenza (62.8%, 27/43), SARS-CoV-2 (34.3%, 12/35), and parainfluenza (31.6%, 12/38). The odds of patients with influenza that have fever was 5.8 times higher than those of patients infected with HRV/HEV (95% CI 2.95-11.40, P<.001). The median duration of medical certificate, light duty, and medical absenteeism were 0 (IQR 0), 2 (IQR 2) and 2 (IQR 0) days, respectively. The odds of patients with influenza having a medical certificate with duration ≥1 day was 5.34 times higher than those in patients with HRV/HEV (95% CI 2.63-10.88, P<.001). No significant differences in the duration of medical absenteeism were found between HRV/HEV and other pathogens. Conclusions: Compared to HRV/HEV, influenza infections were significantly associated with longer medical certificate duration. Nonetheless, there were no significant differences in the overall duration of medical absenteeism across pathogens, as servicemen infected with other pathogens were given light duty instead. These findings emphasize the need for pathogen-agnostic ARI measures. While influenza vaccinations are already mandatory for servicemen in local military camps, encouraging additional public health measures (eg, mask-wearing among symptomatic servicemen, COVID-19 vaccinations, therapeutics) can further reduce ARI incidence, minimize the duration of medical absenteeism, and mitigate the impact on organizational productivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".