Modeling Surface Disinfection Needs To Meet Microbial Risk Reduction Targets.
Modeling Surface Disinfection Needs To Meet Microbial Risk Reduction Targets.
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DOI：
10.1128/aem.00709-18
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发表时间：
2018-09-15
影响因子：
4.4
通讯作者：
Canales RA
中科院分区：
文献类型：
--
作者：
Wilson AM;Reynolds KA;Sexton JD;Canales RA
众所周知，如果按照制造商的说明使用EPA注册的表面消毒剂喷雾剂，可以降低感染风险。然而，目前还没有与表面污染水平相关的卫生保健环境标准。这项研究的意义在于量化所需的减少，以满足各种风险目标，使用现实的病毒浓度表面的卫生保健环境。这项研究通过证明可能需要多种应用来降低风险，并强调需要更多的模型来探索表面微生物污染，患者和医护人员行为以及感染风险之间的关系，从而为清洁方案的设计提供信息。医院内病毒感染是卫生保健获得性感染的重要原因，其中污染物在传播中起作用。使用随机建模来量化表面消毒实践对医院病原体暴露和感染风险的影响，可以为清洁实践提供信息。本研究的目的是预测表面消毒对病毒感染风险的影响，并确定实现风险目标所需的病毒减少。对两例病例的轮状病毒、鼻病毒和甲型流感病毒感染风险进行建模。病例1采用单一污染物接触方法，而病例2假设接触活动6 h。在紧急护理设施中使用环境保护局（EPA）注册的消毒剂进行单轮清洁后，测量到表面和手上的病毒减少了94.1%。该值用于模拟表面消毒干预对感染风险的影响。还模拟了其他表面清洁功效的风险降低。估计了实现风险概率目标所需的表面减少量。在病例1条件下，病毒表面浓度降低94.1%，感染风险降低94.1%。在第2种情况下，表面减少94.1%导致病毒感染风险中位数降低92.96%至94.1%，甲型流感病毒感染风险低于百万分之一。方程中的表面浓度与剂量和感染风险输出高度相关。对于轮状病毒和鼻病毒，需要>99.99%的病毒表面减少才能实现百万分之一的风险目标。本研究量化了相对于表面消毒剂使用的感染风险降低，并证明了低感染剂量微生物的风险目标可能更具挑战性。重要性众所周知，如果按照制造商的说明使用EPA注册的表面消毒剂喷雾剂，可以降低感染风险。然而，目前还没有与表面污染水平相关的卫生保健环境标准。这项研究的意义在于量化所需的减少，以满足各种风险目标，使用现实的病毒浓度表面的卫生保健环境。这项研究通过证明可能需要多种应用来降低风险，并强调需要更多的模型来探索表面微生物污染，患者和医护人员行为以及感染风险之间的关系，从而为清洁方案的设计提供信息。
It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturer's instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks. Nosocomial viral infections are an important cause of health care-acquired infections where fomites have a role in transmission. Using stochastic modeling to quantify the effects of surface disinfection practices on nosocomial pathogen exposures and infection risk can inform cleaning practices. The purpose of this study was to predict the effect of surface disinfection on viral infection risks and to determine needed viral reductions to achieve risk targets. Rotavirus, rhinovirus, and influenza A virus infection risks for two cases were modeled. Case 1 utilized a single fomite contact approach, while case 2 assumed 6 h of contact activities. A 94.1% viral reduction on surfaces and hands was measured following a single cleaning round using an Environmental Protection Agency (EPA)-registered disinfectant in an urgent care facility. This value was used to model the effect of a surface disinfection intervention on infection risk. Risk reductions for other surface-cleaning efficacies were also simulated. Surface reductions required to achieve risk probability targets were estimated. Under case 1 conditions, a 94.1% reduction in virus surface concentration reduced infection risks by 94.1%. Under case 2 conditions, a 94.1% reduction on surfaces resulted in median viral infection risks being reduced by 92.96 to 94.1% and an influenza A virus infection risk below one in a million. Surface concentration in the equations was highly correlated with dose and infection risk outputs. For rotavirus and rhinovirus, a >99.99% viral surface reduction would be needed to achieve a one-in-a-million risk target. This study quantifies reductions of infection risk relative to surface disinfectant use and demonstrates that risk targets for low-infectious-dose organisms may be more challenging to achieve. IMPORTANCE It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturer's instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks.