Epidemiologic Methods: Resistant Nosocomial Infections

描述(由申请人提供)： 抗菌素耐药院内感染(ARNI)的增长需要确定和广泛实施有效的干预措施，以减少其发病率，例如改变处方和改善感染控制。候选干预措施通常通过对ARNI可改变的危险因素的观察性研究来确定；然后在临床试验中对候选干预措施进行评估。现有的观察性研究和临床试验方法都假定患者的结果是相互独立的。对于急性呼吸道感染来说并非如此，因为病原体是可以传播的，所以一个宿主的感染可能会使其他宿主更容易被感染；同样，医院里其他人使用抗生素也会增加个人患急性呼吸道感染的风险，即使S没有接受过药物治疗。我们已经表明，非独立性在ARNI数据中很常见，掩盖了抗菌剂使用对ARNI发病率的机制影响，并且在评估干预措施时可能导致错误的结果(阴性或阳性)；因此，关于许多现有研究的不足和需要更好的方法，出现了一个新的共识。我们将开发和测试考虑患者非独立性的观察性研究和临床试验的方法。对于观察性研究，我们将使用犹他大学(UU)的数据来同时评估单独使用抗生素和整个医院使用对ARNI风险的影响。对于临床试验，我们将开发三种方法来评估干预措施，同时考虑非独立性。我们将在UU和CDC/Emory ICARE项目的真实数据上测试这些方法，并在模拟数据上测试它们与数据的匹配性、检测有效干预的能力以及避免对不真实的干预效果进行误报检测的能力。方法将包括一个自动回归的负二项模型，它很容易在标准软件中实现，以及更复杂的方法，如隐马尔可夫模型。我们将确定在数据上表现良好的方法，并将可靠地确定干预措施的有效性。通过同行评议出版物、免费分发软件、说教研讨会以及未来与具体合作者的合作来传播这些研究的结果，将有助于可靠地确定候选干预措施和评估这些干预措施是否有效的可靠方法。这反过来将导致更好的做法，以减少ARNI的发生率。

DESCRIPTION (provided by applicant): The growth of antimicrobial-resistant nosocomial infections (ARNI) necessitates the identification and widespread implementation of effective interventions to reduce their incidence, such as changes in prescribing and improvements in infection control. Candidate interventions are often identified by observational studies of modifiable risk factors for ARNI; candidates are then evaluated in clinical trials. Existing methods for both observational studies and clinical trials assume that patient outcomes are independent of each other. This is not true for ARNI, because pathogens are transmissible, so infection of one host may make others more likely to be infected; similarly, use of antibiotics by others in the hospital can increase an individual's risk of ARNI, even if s/he has not received the drug. We have shown that nonindependence is common in ARNI data, obscures the mechanistic effects of antimicrobial use on the incidence of ARNI, and can lead to false results (negative or positive) when interventions are assessed; thus, there is an emerging consensus on the inadequacy of many existing studies and the need for better methods. We will develop and test methods for observational studies and clinical trials that account for nonindependence of patients. For observational studies, we will use data from the University of Utah (UU) to assess simultaneously the effects of individual antibiotic use and total hospital-wide use on risk of ARNI. For clinical trials, we will develop three methods for evaluating interventions while accounting for nonindependence. We will test these methods on real data from UU and the CDC/Emory ICARE project, and on simulated data, for their fit to data, ability to detect effective interventions, and ability to avoid false positive detection of intervention effects that are not real. Methods will include an auto regressive negative binomial model, which is easily implemented in standard software, and more sophisticated approaches, such as hidden Markov models. We will identify methods that perform well on data and will reliably determine the effectiveness of interventions. Dissemination of the results of these studies via peer-reviewed publications, free distribution of software, didactic seminars and future work with specific collaborators will aid in the reliable identification of candidate interventions and trustworthy ways to assess whether these interventions work. This will in turn lead to better practices to reduce the incidence of ARNI.