Nursing Intensity of Patient Care Needs and Rates of Healthcare-associated Infections (NIC-HAI)

为了响应PA-12-241，大型医疗保健预防和管理研究项目- 相关感染，这个项目将研究一个重要的临床问题，医疗保健相关感染。 (HAI)，将已收集的现有电子数据用于其他目的，大大降低成本和 提高了研究的效率。而护理人员处于预防和控制的第一线 护理与HAI风险之间关系的研究主要局限于 评估单一因素，如人员配置或遵守具体的循证指南。确实有, 然而，许多系统和单位一级的因素(例如，人员配置水平、工作人员分散注意力、相互竞争的优先事项 繁忙的医疗环境)，这可能会影响护士提供护理和坚持 影响患者安全的做法。发展和扩大卫生信息技术的重要性 承诺进行医疗服务研究，但即使在相同的医疗系统中，数据源通常 “孤立的”和不相连的。我们已经开发了一个电子数据库，其中包括超过9年的约100万名患者出院 从曼哈顿的四家城市医院(2006-2014)，这使得有可能检查复杂的 在整个卫生系统内的关系。在将数据库扩大到包括2015-16年后，目标是 这项拟议的项目是确定急性护理卫生系统内增加 HAI在住院患者中。我们的具体目标是评估HAI与(1)强度的关系 护理需求和人员配备水平以及(2)新发/再发社区病的爆发 传染病，如甲型H1N1流感、埃博拉和麻疹，以及在医院暴露于 具有重要流行病学意义的社区病原体，如结核病、百日咳、脑膜炎、麻疹和诺沃克病毒。 为了评估护理需求强度对HAI风险的影响，我们开发了 测试护理强度指数，该指数包含特定的患者特征、技术和 程序要求和电子记录提供的人员配置因素。感兴趣的结果变量是 从入院到HAI的天数，我们将使用Cox比例风险模型应用生存分析方法 与时间相关的协变量。预测变量包括(1)人口统计学和临床特征 例如入院时患者敏锐度/病情严重程度等；(2)每日患者强度评分；以及(3)每日单位 水平变量：护士人员配置、检查和程序的病人移动，以及重症监护病房。要测试 新出现/再次出现的社区感染对HAI的影响，我们将确定在 新出现的感染和/或疫情调查或准备工作，以预防和控制疫情 医院已经发生了。对此目标感兴趣的结果变量是HAI的数量，分析为 在护理单位--每周一次。我们将应用一个广义线性时间序列模型来检验 使用泊松或负二项模型研究社区感染对HAI发生率的影响。

In response to PA-12-241, Large Research Projects for the Prevention and Management of Healthcare- Associated Infections, this project will examine an important clinical problem, healthcare-associated infections (HAI), using available electronic data already collected for other purposes, greatly reducing the costs and increasing the efficiency of the research. While nursing staff are on the front line of preventing and controlling HAI, studies of the relationship between nursing care and risk of HAI have been limited primarily to assessment of single factors such as staffing or adherence to specific evidence-based guidelines. There are, however, numerous system- and unit-level factors (e.g., staffing levels, distractions, competing priorities within the busy healthcare environment) that may impinge on the ability of nurses to provide care and adhere to practices which affect patient safety. Development and expansion of health information technology holds great promise for health services research, but even within the same healthcare system, data sources are often `siloed' and unlinked. We have developed an electronic database of ~1 million patient discharges over 9 years (2006-2014) from four urban Manhattan hospitals which makes it possible to examine complex relationships within entire health systems. After expanding the database to include 2015-16, the goal of this proposed project is to identify factors within the acute care health systems which increase the risk of HAI among hospitalized patients. Our specific aims are to assess the relationship of HAI with (1) intensity of nursing care demands and staffing levels and (2) outbreaks of emerging/re-emerging community-onset infectious diseases such as influenza A H1N1, Ebola, and measles as well as hospital-based exposures to epidemiologically important community pathogens such as TB, pertussis, meningitis, scabies, and norovirus. To assess the impact of intensity of nursing care demands on risk of HAI, we have developed and tested a Nursing Intensity of Care Index comprised of specific patient characteristics, technologic and procedural demands, and staffing factors available from electronic records. The outcome variable of interest is days from admission to HAI, and we will apply survival analysis methods using a Cox proportion hazard model with time-dependent covariates. The predictor variables include (1) demographic and clinical characteristics such as patient acuity/severity of illness at admission, etc.; (2) daily patient intensity score; and (3) unit daily level variables: nurse staffing, patient movement for tests and procedures, and intensive care unit. To test the impact on HAI of emerging/reemerging community-onset infections, we will identify the time periods during emerging infections and/or outbreak investigations or preparations to prevent and control outbreaks within the hospital have occurred. The outcome variable of interest for this aim is the number of HAI, and the analysis is at the nursing care unit-weekly level. We will apply a generalized linear time series model to examine the impact of community-onset infections on HAI incident rate using Poisson or negative binominal models.