National Centre for Infection Prevention and Management, Division of Infectious Diseases, Section of Infectious Diseases and Immunity, Department of Medicine, Imperial College London, London, UK.
Centre for Patient Safety and Service Quality, Department of Surgery and Cancer, Imperial College London, London, UK.
J Hosp Infect. 2014 Apr;86(4):227-43. doi: 10.1016/j.jhin.2014.01.010. Epub 2014 Feb 26.
Healthcare-associated infections (HCAIs) cause significant morbidity and mortality worldwide, and outbreaks are often only identified after they reach high levels. A wide range of data is collected within healthcare settings; however, the extent to which this information is used to understand HCAI dynamics has not been quantified.
To examine the use of spatiotemporal analyses to identify and prevent HCAI transmission in healthcare settings, and to provide recommendations for expanding the use of these techniques.
A systematic review of the literature was undertaken, focusing on spatiotemporal examination of infectious diseases in healthcare settings. Abstracts and full-text articles were reviewed independently by two authors to determine inclusion.
In total, 146 studies met the inclusion criteria. There was considerable variation in the use of data, with surprisingly few studies (N = 22) using spatiotemporal-specific analyses to extend knowledge of HCAI transmission dynamics. The remaining 124 studies were descriptive. A modest increase in the application of statistical analyses has occurred in recent years.
The incorporation of spatiotemporal analysis has been limited in healthcare settings, with only 15% of studies including any such analysis. Analytical studies provided greater data on transmission dynamics and effective control interventions than studies without spatiotemporal analyses. This indicates the need for greater integration of spatiotemporal techniques into HCAI investigations, as even simple analyses provide significant improvements in the understanding of prevention over simple descriptive summaries.
医疗保健相关感染(HAI)在全球范围内导致了大量的发病率和死亡率,并且通常只有在感染达到较高水平后才会被发现。医疗机构内会收集大量数据,但这些信息在多大程度上被用于了解 HAI 动态尚未被量化。
探讨时空分析在识别和预防医疗机构中 HAI 传播的应用,并为扩大这些技术的应用提供建议。
对文献进行了系统回顾,重点关注医疗机构中传染病的时空研究。两位作者分别对摘要和全文文章进行了独立审查,以确定是否符合纳入标准。
共有 146 项研究符合纳入标准。数据的使用存在很大差异,令人惊讶的是,只有少数研究(N=22)使用时空特定分析来扩展 HAI 传播动态的知识。其余 124 项研究为描述性研究。近年来,统计分析的应用有了适度的增加。
时空分析在医疗机构中的应用有限,只有 15%的研究包含任何此类分析。分析性研究提供了关于传播动态和有效控制干预的更多数据,而没有时空分析的研究则提供了较少的数据。这表明需要将时空技术更深入地整合到 HAI 调查中,因为即使是简单的分析也能显著提高对预防的理解,而不是简单的描述性总结。