Epidemiologic Methods: Resistant Nosocomial Infections

描述（由申请人提供）： 抗生素耐药性医院感染（ARNI）的增长需要确定和广泛实施有效的干预措施，以减少其发生率，如改变处方和改善感染控制。候选干预措施通常通过对ARNI可改变风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。观察性研究和临床试验的现有方法都假设患者结局彼此独立。这对于ARNI来说并不正确，因为病原体是可传播的，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，医院中其他人使用抗生素会增加个体患ARNI的风险，即使他/她没有接受药物治疗。我们已经表明，非独立性在ARNI数据中很常见，模糊了抗菌药物使用对ARNI发病率的机械作用，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，对许多现有研究的不足以及需要更好的方法正在形成共识。我们将开发和测试用于观察性研究和临床试验的方法，以解释患者的非独立性。对于观察性研究，我们将使用来自犹他州大学（UU）的数据，同时评估单个抗生素使用和整个医院使用对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。我们将测试这些方法的真实的数据，从UU和CDC/埃默里ICARE项目，并对模拟数据，他们适合的数据，检测有效的干预措施的能力，并能够避免假阳性检测干预效果不是真实的。方法将包括一个自回归负二项模型，这是很容易实现的标准软件，和更复杂的方法，如隐马尔可夫模型。我们将确定在数据上表现良好的方法，并可靠地确定干预措施的有效性。通过同行评审的出版物、免费分发软件、教学研讨会和未来与特定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。这将反过来导致更好的做法，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.