Patient referral patterns and the spread of hospital-acquired infections through national health care networks.
Patient referral patterns and the spread of hospital-acquired infections through national health care networks.
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DOI：
10.1371/journal.pcbi.1000715
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发表时间：
2010-03-19
影响因子：
4.3
通讯作者：
Grundmann H
中科院分区：
文献类型：
--
作者：
Donker T;Wallinga J;Grundmann H
医院获得性感染率，如耐甲氧西林金黄色葡萄球菌（MRSA），越来越多地被用作医院卫生质量指标。或者，这些比率可能因医院而异，因为医院在潜在殖民患者的入院和转诊方面有所不同。我们评估了医疗保健网络中医院之间的不同转诊模式是否会影响耐甲氧西林金黄色葡萄球菌（MRSA）等医院获得性感染的发生率。我们使用2004年的荷兰医疗注册来衡量医院之间的连通性。这使我们能够重建荷兰的医院网络。我们使用数学模型来评估不同患者转诊模式对医院之间以及医院类别（大学医疗中心，顶级临床医院和综合医院）之间医院获得性感染潜在传播的影响。与教学医院或综合医院相比，大学医院有更多的共享病人，因此更有可能成为第一批接收殖民病人的医院。此外，由于该网络是面向大学医院的，即使所有医院的感染控制措施同样有效，大学医院的感染率也较高。患者转诊模式对医疗保健相关感染（如医院获得性MRSA）的传播具有深远的影响。因此，MRSA患病率在医院之间存在差异，因为每个医院在医疗保健网络中的位置不同。因此，医院之间MRSA比率的任何比较，作为医院卫生的基准，都应该考虑到医院在网络中的位置。人们普遍认为，医院获得性感染的患病率反映了个别医院的医疗保健质量，因此通常用作基准。直觉上，这种想法是，感染在医疗质量差的医院更容易传播。这是假设所有医院的入院病人引入新感染的比率相同。在这篇文章中，我们证明了这个假设不太可能是正确的。利用全国患者入院数据，我们能够重建由医院之间转诊的患者组成的整个医院网络。该网络显示，大学医院接纳了更多最近在其他医院住院的患者。因此，他们更有可能接纳那些仍然携带在以前住院期间获得的病原体的患者。因此，感染的流行率不仅反映了保健的质量，也反映了病人与转诊医院的联系。这一现象在单个医院层面被忽略;我们的研究是第一个解决医院之间的联系，以解释医院获得性感染的患病率。我们的研究结果意味着，干预措施应集中在医院，在病人转诊网络的中心。
Rates of hospital-acquired infections, such as methicillin-resistant Staphylococcus aureus (MRSA), are increasingly used as quality indicators for hospital hygiene. Alternatively, these rates may vary between hospitals, because hospitals differ in admission and referral of potentially colonized patients. We assessed if different referral patterns between hospitals in health care networks can influence rates of hospital-acquired infections like MRSA. We used the Dutch medical registration of 2004 to measure the connectedness between hospitals. This allowed us to reconstruct the network of hospitals in the Netherlands. We used mathematical models to assess the effect of different patient referral patterns on the potential spread of hospital-acquired infections between hospitals, and between categories of hospitals (University medical centers, top clinical hospitals and general hospitals). University hospitals have a higher number of shared patients than teaching or general hospitals, and are therefore more likely to be among the first to receive colonized patients. Moreover, as the network is directional towards university hospitals, they have a higher prevalence, even when infection control measures are equally effective in all hospitals. Patient referral patterns have a profound effect on the spread of health care-associated infections like hospital-acquired MRSA. The MRSA prevalence therefore differs between hospitals with the position of each hospital within the health care network. Any comparison of MRSA rates between hospitals, as a benchmark for hospital hygiene, should therefore take the position of a hospital within the network into account. The prevalence of hospital acquired infections is widely believed to reflect the quality of health care in individual hospitals, and is therefore often used as a benchmark. Intuitively, the idea is that infections spread more easily in hospitals with a poor quality of health care. This assumes that the rate at which admitted patients introduce new infections is the same for all hospitals. In this article, we show that this assumption is unlikely to be correct. Using national data on patient admissions, we are able to reconstruct the entire hospital network consisting of patients referred between hospitals. This network reveals that university hospitals admit more patients that recently stayed in other hospitals. Consequently, they are more likely to admit patients that still carry pathogens acquired during their previous hospital stay. Therefore, the prevalence of infections does not only reflect the quality of health care but also the connectedness to hospitals from which patients are referred. This phenomenon is missed at the single hospital level; our study is the first to address the connectedness between hospitals in explaining the prevalence of hospital acquired infections. Our findings imply that interventions should focus on hospitals that are central in the network of patient referrals.
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影响因子：
11.8
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通讯作者：
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DOI：
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2007-10
影响因子：
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