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作者:

Zhao, Fangli (Zhao, Fangli.) | Zhang, Nan (Zhang, Nan.) | Wu, Yadi (Wu, Yadi.) | Dou, Zhiyang (Dou, Zhiyang.) | Cao, Bing (Cao, Bing.) | Luo, Yingjie (Luo, Yingjie.) | Lu, Yan (Lu, Yan.) | Du, Li (Du, Li.) | Xiao, Shenglan (Xiao, Shenglan.)

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Scopus SCIE

摘要:

Background: Dental outpatient departments, characterized by close proximity and unmasked patients, present a considerable risk of respiratory infections for health care workers (HCWs). However, the lack of comprehensive data on close contact (< 1.5 m) between HCWs and patients poses a significant obstacle to the development of targeted control strategies. Methods: An observation study was conducted at a hospital in Shenzhen, China, utilizing depth cameras with machine learning to capture close-contact behaviors of patients with HCWs. Additionally, questionnaires were administered to collect patient demographics. Results: The study included 200 patients, 10 dental practitioners, and 10 nurses. Patients had significantly higher close-contact rates with dental practitioners (97.5%) compared with nurses (72.8%, P < .001). The reason for the visit significantly influenced patient-practitioner (P = .018) and patient-nurse (P = .007) closecontact time, with the highest values observed in prosthodontics and orthodontics patients. Furthermore, patient age also significantly impacted the close-contact rate with nurses (P = .024), with the highest rate observed in patients below 14 years old at 85% [interquartile range: 70-93]. Conclusions: Dental outpatient departments exhibit high HCW-patient close-contact rates, influenced by visit purpose and patient age. Enhanced infection control measures are warranted, particularly for prosthodontics and orthodontics patients or those below 14 years old. (c) 2024 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词:

Respiratory infection Infection control Close contact behavior Dental setting Machine learning

作者机构:

  • [ 1 ] [Zhao, Fangli]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Shenzhen Key Lab Pathogen Microbes & Biosafety, Shenzhen Campus, Shenzhen, Peoples R China
  • [ 2 ] [Wu, Yadi]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Shenzhen Key Lab Pathogen Microbes & Biosafety, Shenzhen Campus, Shenzhen, Peoples R China
  • [ 3 ] [Luo, Yingjie]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Shenzhen Key Lab Pathogen Microbes & Biosafety, Shenzhen Campus, Shenzhen, Peoples R China
  • [ 4 ] [Xiao, Shenglan]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Shenzhen Key Lab Pathogen Microbes & Biosafety, Shenzhen Campus, Shenzhen, Peoples R China
  • [ 5 ] [Zhao, Fangli]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Guangzhou, Peoples R China
  • [ 6 ] [Wu, Yadi]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Guangzhou, Peoples R China
  • [ 7 ] [Luo, Yingjie]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Guangzhou, Peoples R China
  • [ 8 ] [Xiao, Shenglan]Sun Yat Sen Univ, Sch Publ Hlth Shenzhen, Guangzhou, Peoples R China
  • [ 9 ] [Zhang, Nan]Beijing Univ Technol, Beijing Key Lab Green Built Environm & Energy Effi, Beijing, Peoples R China
  • [ 10 ] [Cao, Bing]Beijing Univ Technol, Beijing Key Lab Green Built Environm & Energy Effi, Beijing, Peoples R China
  • [ 11 ] [Dou, Zhiyang]Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
  • [ 12 ] [Lu, Yan]Sun Yat Sen Univ, Affiliated Hosp 7, Acquired Infect Control Dept, Shenzhen, Peoples R China
  • [ 13 ] [Du, Li]Sun Yat Sen Univ, Affiliated Hosp 7, Acquired Infect Control Dept, Shenzhen, Peoples R China

通讯作者信息:

  • [Xiao, Shenglan]66 Gongchang Rd, Shenzhen 518107, Guangdong, Peoples R China;;

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来源 :

AMERICAN JOURNAL OF INFECTION CONTROL

ISSN: 0196-6553

年份: 2024

期: 11

卷: 52

页码: 1296-1301

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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