Health Security Preparedness For Biological Threats In Healthcare Facilities: Surveillance, Containment, And Response Capacity
Notice bibliographique
Résumé
Background: The contemporary global health security landscape is characterized by an intensifying frequency and complexity of biological threats, ranging from naturally occurring high-consequence infectious diseases (HCIDs) like Ebola and SARS-CoV-2 to the persistent specter of bioterrorism. Healthcare facilities (HCFs) constitute the operational frontline of biodefense, yet their capacity to effectively detect, contain, and respond to these threats remains critically uneven across geopolitical and economic divides. The convergence of workforce attrition, "just-in-time" logistical fragility, and infrastructural obsolescence has exposed profound vulnerabilities in the hospital sector's ability to maintain continuity of care under biological stress. Objectives: This comprehensive systematic review aims to evaluate the global state of health security preparedness within healthcare facilities. The primary objectives are to: (1) assess the efficacy of existing surveillance architectures—specifically comparing syndromic surveillance in high-resource settings against Integrated Disease Surveillance and Response (IDSR) frameworks in low-resource settings—in facilitating early threat detection; (2) analyze containment capacities by contrasting the engineering and operational outcomes of High-Level Isolation Units (HLIUs) versus standard infection control wards; and (3) evaluate response capacity through the lenses of workforce resilience, personal protective equipment (PPE) compliance, and supply chain sustainability. Methods: A systematic literature review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A multi-database search (PubMed, Scopus, Web of Science, Google Scholar) was executed for peer-reviewed and grey literature published through 2023. Included studies encompassed randomized trials, observational cohorts, and qualitative assessments of hospital preparedness globally. Quality assessment was rigorously performed using the Cochrane Risk of Bias tool (RoB 2) for interventional studies and the Newcastle-Ottawa Scale (NOS) for observational research, ensuring a weighted synthesis of high-quality evidence. Results: The review synthesized data from a diverse array of global studies. In the domain of surveillance, syndromic systems in high-income nations demonstrated the capacity to predict Intensive Care Unit (ICU) surges by 11–13 days, though with significant specificity trade-offs (often ~50% detection probability for covert bioterrorism by Day 2). Conversely, IDSR implementation in sub-Saharan Africa showed marked improvements in reporting completeness (rising from 84.5% to 96% in Sierra Leone) but remained hampered by a lack of laboratory integration and feedback loops. Containment analysis revealed that HLIUs achieve aerosol containment rates exceeding 99.7% and significantly lower healthcare worker (HCW) infection rates (7% vs. 11% in general wards) yet are operationally non-scalable. Response capacity assessment identified a critical "preparedness decay," characterized by PPE compliance rates as low as 21.64% in observational audits despite high theoretical knowledge, and pervasive workforce burnout, with over 96% of staff reporting anxiety during surges. Conclusion: Health security preparedness in healthcare facilities is currently defined by a "hardware-software" dissonance. While advanced engineering solutions and theoretical frameworks exist ("hardware"), they are critically undermined by the "human factor" ("software")—specifically, behavioral non-compliance, psychological exhaustion, and the inequitable distribution of resources. Achieving genuine bioresilience requires a paradigm shift from reactive, agent-specific planning to a sustained, all-hazards approach that prioritizes workforce protection, integrates real-time diagnostics with syndromic signals, and establishes resilient, equitable supply chains independent of crisis-driven funding cycles.
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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 | 0,010 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».