Nursing Interventions to Reduce Disparities in Hospital Outcomes

描述(由申请人提供)：这是ARRA对P30-NR005043护理结果研究中心的竞争性修订。我们寻求扩大父母拨款的范围，与制图建模实验室(CML)和人文地理和空间分析方面的研究人员建立新的跨学科合作，并将一种新的方法--地理信息系统(GIS)技术应用于护理结果研究，重点是减少医院结果中的种族和民族差异。我们的工作是由最近的研究推动的，该研究表明，与患者的个体特征相比，医院的特征是医院护理结果中种族/民族不平等的重要决定因素。我们想要用比以前更完整的数据进一步探索单个患者的社会经济状况对预后的影响，以及按医院划分的患者亚组的构成影响。后者涉及的证据表明，在照顾很大比例的少数族裔患者的医院里，所有患者的结果都更差。我们将把2005-06年度4个州859家医院的数据库中数百万患者的邮政编码与CML提供的关于他们所在社区的各种数据(包括收入和教育)的详细信息进行匹配，并计算从他们的家到他们接受护理的医院的距离。同样，我们将根据护士的邮政编码，包括从他们家中到雇用医院的旅行时间，来增加关于在这些医院执业的护士的全面调查信息。最后，我们将通过详细的医院位置和市场区域信息来丰富我们的医院信息。拟议的研究有以下目的：1)估计行政数据库中未包括的患者特征(如社会经济地位和从家到医院的旅行时间)，以努力更多地了解以前未测量的患者及其社区特征如何影响医院护理结果；2)确定可能导致结果差异的医院特征(教师地位和规模之外)，包括绘制护士相对于医院位置的地理分布图，护士是否在工作环境良好的医院工作的时间更长，以及与医院位置相关的社会经济条件和市场因素；3)将空间分析得到的新信息整合到CNOR研究人员先前开发的医院结果预测模型中，以揭示有针对性的护理资源干预在多大程度上可以成功地减少差异。

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.