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Investigating COVID-19 transmission in a tertiary hospital in Hanoi, Vietnam using social network analysis.
Hoang, Ngoc-Anh Thi; Pham, Thai Quang; Quach, Ha-Linh; Hoang, Ngoc Van; Nguyen, Khanh Cong; Dang, Duc-Anh; Vogt, Florian.
Afiliación
  • Hoang NT; National Centre for Epidemiology and Population Health, College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia.
  • Pham TQ; Department of Communicable Diseases Control, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.
  • Quach HL; Department of Communicable Diseases Control, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.
  • Hoang NV; Department of Biostatistics and Medical Informatics, School of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
  • Nguyen KC; National Centre for Epidemiology and Population Health, College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia.
  • Dang DA; Department of Communicable Diseases Control, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam.
  • Vogt F; The General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam.
Trop Med Int Health ; 27(11): 981-989, 2022 11.
Article en En | MEDLINE | ID: mdl-36181386
OBJECTIVES: In March 2020, a COVID-19 outbreak in a major referral hospital in Hanoi, Vietnam led to 7664 patients and staff being sent into lockdown for 2 weeks, and more than 52,200 persons across 49 provinces being quarantined. We assessed SARS-CoV-2 transmission patterns during this to-date largest hospital outbreak in Vietnam using social network analysis (SNA). METHODS: We constructed a directed relational network and calculated network metrics for 'degree', 'betweenness', 'closeness' and 'eigenvector' centrality to understand individual-level transmission patterns. We analysed network components and modularity to identify sub-network structures with disproportionately big effects. RESULTS: We detected 68 connections between 46 confirmed cases, of whom 27 (58.7%) were ancillary support staff, 7 (15.2%) caregivers, 6 (13%) patients and 2 (4.4%) nurses. Among the 10 most important cases selected by each SNA network metric, transmission dynamics clustered in 17 cases, of whom 12 (70.6%) cases were ancillary support staff. Ancillary support staff also constituted 71.1% of cases in the dominant sub-network and 68.8% of cases in the three largest sub-communities. CONCLUSIONS: We identified non-clinical ancillary support staff, who are responsible for room service and food distribution in hospital wards in Vietnam, as a group with disproportionally big impacts on transmission dynamics during this outbreak. Our findings call for a holistic approach to nosocomial outbreak prevention and response that includes both clinical and non-clinical hospital staff. Our work also shows the potential of SNA as a complementary outbreak investigation method to better understand infection patterns in hospitals and similar settings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Trop Med Int Health Asunto de la revista: MEDICINA TROPICAL / SAUDE PUBLICA Año: 2022 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Trop Med Int Health Asunto de la revista: MEDICINA TROPICAL / SAUDE PUBLICA Año: 2022 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido