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

根据PA-12-241，预防和管理医疗保健的大型研究项目- 相关感染，这个项目将研究一个重要的临床问题，卫生保健相关感染 (HAI)，利用已收集作其他用途的电子资料，大大减低成本， 提高研究效率。护理人员在防控的第一线 HAI，护理和HAI风险之间关系的研究主要限于 评估单一因素，如人员配备或遵守具体的循证指南。有， 然而，许多系统和单元级因素（例如，人员配备水平，分心，内部竞争的优先事项 忙碌的医疗环境），这可能会影响护士提供护理和坚持 影响患者安全的措施。卫生信息技术的发展和扩大对促进 医疗服务研究的承诺，但即使在同一个医疗保健系统，数据源往往是 “孤立”和不联系。我们已经开发了一个电子数据库，其中包含9年以上约100万例患者的出院数据。 年（2006-2014年）从四个城市曼哈顿医院，这使得有可能检查复杂的 在整个卫生系统中。在扩大数据库以包括2015-16年之后， 该拟议项目旨在确定急性护理卫生系统中增加以下风险的因素： 住院患者中的HAI。我们的具体目标是评估HAI与（1） 护理需求和人员配备水平和（2）新出现/重新出现的社区发病的爆发 感染性疾病，如甲型H1N1流感、埃博拉病毒和麻疹，以及医院接触 流行病学上重要的社区病原体，如结核病、百日咳、脑膜炎、疥疮和诺如病毒。 为了评估护理需求强度对HAI风险的影响，我们制定了 测试了护理强度护理指数，包括特定的患者特征，技术和 程序要求和电子记录中的人员编制因素。感兴趣的结果变量是 从入院到HAI的30天，我们将使用考克斯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 例如患者入院时病情的紧急程度/严重程度等; (2)每日患者强度评分;和（3）每日单位 水平变量：护士配备，病人移动测试和程序，和重症监护病房。测试 新出现/重新出现的社区发病感染对HAI的影响，我们将确定 对新出现的感染和/或疫情进行调查或做好准备，以预防和控制 医院发生了。这一目标的结果变量是HAI的数量，分析如下： 在护理单元每周一次我们将应用广义线性时间序列模型来研究 使用泊松或负二项模型研究社区发病感染对HAI发病率的影响。

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.