Nursing Interventions to Reduce Disparities in Hospital Outcomes

描述（由申请人提供）：这是P30-NR 005043护理结果研究中心的ARRA竞争性修订版。我们寻求扩大父母补助金的范围，以创建一个新的跨学科的合作与制图建模实验室（CML）和研究人员在人文地理和空间分析，并应用一种新的方法，地理信息系统（GIS）技术，护理成果的研究重点是减少种族和民族的差距在医院的结果。我们的工作的动机是最近的研究表明，医院的功能，与患者的个人特征相比，是重要的决定因素，种族/民族不平等的结果，医院护理。我们希望进一步探索，并与更完整的数据比以前，个体患者的社会经济地位对结果的影响，以及由医院的患者亚组的聚合的组成效应。后者解决的证据表明，结果是更糟糕的所有患者在医院照顾了很大比例的少数民族患者。我们将把2005-06年数据库中4个州859家医院的数百万患者的邮政编码与CML的详细信息进行匹配，这些详细信息涉及他们社区的各种数据，包括收入和教育，我们将计算从他们家到他们接受治疗的医院的距离。同样，我们将增加我们的全面调查为基础的信息，护士谁在这些医院的做法，从他们的邮政编码信息，包括旅行时间从他们的家到他们的雇主医院。最后，我们将丰富我们关于医院的信息，提供有关其位置和市场领域的详细信息。本研究的目的如下：1）评估未纳入管理数据库的患者特征（如社会经济地位和从家到医院的时间），以了解更多关于患者及其社区以前未测量的特征如何影响医院护理的结果;（2）确定医院特色（除了教学地位和规模），这可能会导致结果的差异，包括绘制护士相对于医院位置的地理分布图，护士是否需要更长的时间在具有良好工作环境的医院工作，以及与医院位置相关的社会经济条件和市场因素; 3）将来自空间分析的新信息整合到CNOR调查人员先前开发的医院结局预测模型中，以揭示有针对性的护理资源干预在多大程度上可以成功减少差异。

DESCRIPTION (provided by applicant): This is an ARRA competitive revision of P30-NR005043 Center for Nursing Outcomes Research. We seek to extend the scope of the parent grant to create a new interdisciplinary collaboration with the Cartographic Modeling Laboratory (CML) and investigators in human geography and spatial analysis and to apply a novel approach, Geographic Information Systems (GIS) technology, to nursing outcomes research focused on reducing racial and ethnic disparities in hospital outcomes. Our work is motivated by recent research suggesting that features of hospitals, as contrasted with individual characteristics of patients, are important determinants of racial/ ethnic inequalities in outcomes of hospital care. We want to explore further, and with more complete data than has been available previously, the effects of socioeconomic status of individual patients on outcomes as well as the compositional effects of aggregations of sub-groups of patients by hospital. The latter addresses evidence that outcomes are worse for all patients in hospitals that care for a large proportion of minority patients. We will match the zip codes of the millions of patients in our 2005-06 database of 859 hospitals in 4 states with detailed information from CML on a variety of data about their neighborhoods, including income and education, and we will compute the distance from their home to the hospital where they received care. Likewise we will add to our comprehensive survey-based information on nurses who practice at these hospitals by deriving from their zip codes information including the travel time from their homes to their employing hospitals. Finally, we will enrich our information about hospitals with detailed information on their locations and market areas. The proposed study has the following aims: 1) to estimate patient characteristics not included in administrative databases (such as socioeconomic status and time traveled from home to hospital) in an effort to learn more about how previously unmeasured characteristics of patients and their communities impact outcomes of hospital care; 2) to determine hospital characteristics (beyond teaching status and size) that potentially contribute to disparities in outcomes including mapping the geographic distribution of nurses relative to hospital location, whether nurses travel longer to work at hospitals with good work environments, and socioeconomic conditions and market factors associated with hospital location; and 3) to integrate the new information derived from spatial analyses into predictive models of hospital outcomes developed previously by CNOR investigators to shed new light on to what extent targeted nursing resource interventions could be successful in reducing disparities.