Yue Li, Ph.D. - US grants

[unreadable] DESCRIPTION (provided by applicant): Quality of long-term care received by nursing home (NH) residents remains a persistent concern. In November 2002, the Centers for Medicare and Medicaid Services (CMS) launched a national report with NH quality measures (QMs) -the "Nursing Home Compare" Web site, which publishes key outcomes measures of quality derived from the Minimum Data Set (MDS). The NH quality report is expected to empower consumers to choose NH services based on quality, and to stimulate quality improvement through market competition. Given its potential impact, it is critical that the report card quality measures accurately differentiate homes with good quality from those with poor quality. Since health outcomes are determined by both care quality and resident frailties and comorbidities, it is necessary to adjust for case mix variations between facilities before their outcomes are compared. The online NH QMs, however, take no or only minimal steps to adjust for resident characteristics. This study proposes to examine the correlations between the national "Nursing Home Compare" QMs and quality measures based on the same outcomes but incorporating extensive risk adjustment. To fit within the scope of an RO3, this study will focus on only one of the chronic care measures currently reported - percent of residents in a nursing home whose need for help with activities of daily life (ADL) has increased. The methods developed in this project and the lessons learned would serve as a basis for a later RO1 that would examine the other published quality measures. The specific aim of the proposed study is to test the hypothesis that nursing homes' quality rankings and outlier status based on the published "Nursing Home Compare" quality measure of ADL status are different from rankings and outlier status based on the measure of the same outcomes with comprehensive risk adjustment. This study will use the national MDS data to develop comprehensively risk-adjusted measures based on state-of-the-art statistical regression methods, and demonstrate the feasibility and examine the implications of using such measures for "Nursing Home Compare." Findings of the proposed study will contribute to efforts to develop improved, valid and credible risk-adjusted outcome-based measures of quality that are likely to be effective in public report cards. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Nursing home quality has been and continues to be a major concern for patients, their families, and health care policy makers. Over the last 2 decades, both the federal and state governments have engaged in efforts to address these concerns. These include the implementation of standard resident assessment and care planning tools, reforms of the state regulatory process, national and state publications of nursing home quality measures, national quality improvement activities by the Quality Improvement Organizations, and most recently the development of technical assistance (TA) programs in many states aimed at providing collaborative or non-punitive on-site consultation, training or sharing of best-practices with nursing facility staff. Despite these varied efforts, many quality problems persist, suggesting the need to understand the strengths and weaknesses of each of these initiatives. This study is designed to test the impact of the state initiated TA programs and their potential to contribute to better nursing home care. Nursing home TA programs vary substantially across states in terms of program structure (e.g., staffing pattern) and process (e.g., whether results of TA visits to facilities are shared with the state regulatory agency). Thus, their effectiveness is likely to vary as well. The natural variation in program characteristics offers us the opportunity to study the relationship between key components of the state TA program and high quality nursing home care. The main objective of the project is to inform policy by identifying aspects of the state TA program that contribute to better nursing home care. Specific aims are to test the hypotheses that the key structural and process characteristics of state TA programs are associated with better risk-adjusted health outcomes of nursing home residents and lower case mix adjusted deficiency citations. Data will be collected from survey of state TA programs and several secondary databases. Multivariate regression analyses including instrumental variable analysis will be used for hypothesis testing. PUBLIC HEALTH RELEVANCE: This study will examine the impact of the key structural and process components of state technical assistance programs on nursing home risk-adjusted health outcomes and case mix adjusted deficiency citations. We will combine data from different sources including a survey of state TA programs, and use statistical regression techniques including instrumental variable analysis for hypothesis testing.

DESCRIPTION (provided by applicant): Medical or surgical patients with coexisting mental disorders face significant barriers to accessing appropriate health care in general, and high quality cardiac services specifically. Evidence suggests that after acute myocardial infarction, patients with mental disorders tend to under-use invasive cardiac procedures including coronary artery bypass graft (CABG) surgery. In addition, our recent work shows that patients with mental disorders (such as psychiatric disorders or substance abuse disorders), when they do receive CABG surgery, are more likely to be treated by cardiac surgeons of inferior quality of care. The mental-disorder related disparities in quality of cardiac revascularization surgery are of particular concern for the Medicare population, as over 25% older persons are suffering mental disorders, and coronary heart disease (CHD) is one of the major causes of medical morbidity and mortality in this population. In addition, over 50% of all CABG surgeries are performed each year on individuals age 65 and over. However, relatively little is known about the dynamics underlying the disparity in quality of CABG surgery received by patients with coexisting mental disorders, a medically vulnerable group who has increasingly been shown to suffer from inappropriate medical and surgical care. The purpose of the study is to contribute to the understanding of the causes that lead to these observed disparities and the related clinical and policy implications, through an examination of physician behaviors in referring patients to CABG surgery. We will analyze Medicare claims data to examine actual physician practices, in which physician behavior in referring CHD patients with and without mental disorders will be compared. Multivariate statistical regression analyses will be employed. Findings of this study will help the design of future studies that aim to inform effective policy and practice initiatives to eliminate the disparities faced by elderly patients with mental disorders and better equip policy makers to address problems in the delivery of medical care to the vulnerable, mentally ill patients. PUBLIC HEALTH RELEVANCE: This proposed project was designed to examine the role of referring physicians in the mental-disorder related disparities in receipt of high-quality cardiac procedures among Medicare patients with coronary heart diseases.

1050443
Li

This award supports a planning visit to enable Professor Yue Li at Michigan Technological University to meet with Professor Mark Steward, Director of the Centre for Infrastructure Performance and Reliability in Australian, Professor Garry Willgoose, Director, Centre for Climate Impact Management at the University of Newcastle in Australia and Dr. Xiaoming Wang at the Commonwealth Scientific and Industrial Research Organization Climate Adaptation Flagship and Sustainable Ecosystems. The goal of the project is to support the development of a collaborative research partnership between the U.S. and Australia for climate-related infrastructure adaptation for resistance to natural hazards. Evidence suggests that natural hazards such as hurricanes (cyclones), snow and heavy precipitation, and flood are expected to change in frequency and intensity with the smallest rise in temperature resulting from climate change. These extreme events can result in severe damage to infrastructure, including buildings, roads, bridges and utilities, especially because current design practice assumes a stationary process and no time-dependent changes to hazard occurrence probabilities. The collaborative research will develop a long-term partnership with numerous research opportunities for U.S. early-career faculty and students. Specifically, it will 1) advance understanding of risks due to climate change that accommodate the uncertainty of climate change and establish the relationship between loss estimation and the changing patterns of natural hazards, and 2) introduce new opportunities for engineering-socioeconomic collaborations on climate risk assessment and develop a decision framework for economic assessment of climate adaptation measures.

The results of this research will help to facilitate sustainability-oriented long term planning and short term disaster-response measures/strategies and provide new, research-based information to inform the decision making process for cost-effective climate change adaptation strategies. There is sufficient overlap of interests between researchers in the U.S. and Australia to indicate that they can successfully pursue the activities proposed, and that the interaction will benefit both sides. In addition, the involvement of two U.S. graduate students will enable them to gain a valuable international perspective and insight. They will also participate in discussions on future collaborative activities with the researchers.

How does the natural world maintain its astonishing diversity, especially among species that directly compete with each other? Theory predicts that constant changes to the physical environment over time and space and the transition from one scale to another (smaller to larger space, shorter to longer time) can account for species coexistence. To date, this prediction lacks rigorous empirical tests. The proposed project will use multi-scale field experiments and mathematical modeling based on field data to test the hypothesis that smaller scale environmental variation promotes broad-scale coexistence between invasive and native desert winter annual plants in the Sonoran Desert. Researchers will combine stable isotope analysis, aerial photographs, and field experiments to quantify a key mechanism underlying coexistence and the maintenance of biodiversity.

The project will provide a powerful model for scientists to conduct rigorous empirical tests on species coexistence across a broad spectrum of natural communities. Together with addressing a fundamental question in ecology, the project will foster an understanding of the dynamic nature of biological invasions, highlighting conditions under which an invasion is likely to cause problem. By informing land managers of those conditions, the project offers practical solutions to managing invasive species. The project is a collaboration between the University of Arizona and the US Marine Corps Air Station, Yuma, and results will be shared with military units and government agencies that must manage invasive species. This project will also train four undergraduate students, introducing them to the rigor and excitement of scientific discovery.

DESCRIPTION (provided by applicant): Project Summary/Abstract Racial/ethnic minorities made up about 16 percent of the nursing home population in 2004. More recent data suggest that the number of minority nursing homes residents is increasing dramatically, while the number of white residents is declining. These new demographic changes raise concerns about whether nursing homes are able to serve appropriately the needs of patients with increasingly diverse ethnic and cultural backgrounds. The quality of care delivered in nursing homes has been a longstanding issue plaguing the industry, and continues to be the focus of federal and state policies aimed at improving it. Extant evidence suggests that these policies, including strengthened state quality regulations, improved Medicaid reimbursements, and more recent efforts to foster market competition (e.g., the repeal of the Certificate of Need requirement; the national quality reporting), have improved nursing home quality and outcomes during the past decade. As the industry makes headway and improves quality in response to these policy and market trends, it is important to recognize that improvements in overall nursing home quality may not automatically benefit all subgroups of patients equally. Indeed, despite evidence of overall improvements, a large body of literature continues to document racial/ethnic disparities in nursing home quality. In particular, our recent work, published in the Journal of American Medical Association, has for the first time provided evidence that while pressure ulcer outcomes in nursing homes have improved for both non-Hispanic whites and minorities, the gap between these two groups of patients remained unchanged. Thus, the overall goal of this proposal is to answer two very important questions: 1) Does this phenomenon, of improvement in quality for all concomitant with a persistent gap between racial/ethnic groups, exist in other areas of known disparities for nursing home care? and 2) Can we identify specific policy and market characteristics that have been successful not only in improving quality but also in closing the racial and ethnic gap in quality? The proposed project will achieve these goals by investigating the longitudinal and cross sectional impact of each of three major types of nursing home quality drivers - state regulation of quality, Medicaid reimbursement, and market competition - on the racial/ethnic quality gap and its persistence. The information generated by this project will contribute to the knowledge regarding the ways with which major nursing home policies impact equity of care.

Providing reliable clean water is essential for the health and prosperity of communities. Our society, however, frequently faces the disastrous consequences of decisions made without considering their socio-economic context or the interdependency of critical infrastructure systems and services. Examples include incidents of poisonous algae in the water supply in Toledo, OH in summer 2014 and more recently, the lead contaminated water in Flint, MI which led to a declaration of a State of Emergency by the federal government. A common observation is that decisions that are primarily based on short-term cost considerations, such as those made by many municipal and city offices when facing resource constraints, can exacerbate water problems rather than improve them. This transdisciplinary research develops a water infrastructure investment decision support (WIIDS) model, calibrated with a rich repository of data. WIIDS places sustainable water services in a broad socioeconomic context that considers the inherent interdependencies of systems and interactions with the contractual, physical, and service infrastructures essential to communities.

This research formulates an advanced decision model that builds on a large repository of data to support water-related decisions at different levels. Particularly, the model describes the interdependencies of water systems, road systems (that provide mobility of people and goods while applying loads on the water pipes via mechanical and environmental processes), and the contractual systems (that affect community service decisions that influence the durability of water pipes). The model consists of two distinct parts. One is an engineering sub-model that uses historical data and forecast conditions to predict the probability of failure and the associated costs for the selected sections of water supply infrastructure. Another is a socioeconomic sub-model that utilizes input from the engineering sub-model along with current and forecast business impacts, traffic impact, community demographics, and critical infrastructures (e.g., hospitals, schools) to provide holistic repair/replace/reroute recommendations. The model allows comparisons among alternative decisions (e.g. reactive vs predictive maintenance) considering social costs, indirect economic costs, and public health implications besides the direct solution costs.

Project Summary/Abstract Suicide is among the 10 leading causes of death in the U.S. and suicide risk increases substantially after age 65. Nearly 70% of individuals currently 65 years old will require long-term care during the remainder of their lives. The nation's 15,000 nursing homes are an important setting providing residential post-acute and long-term care to over 3 million older and disabled Americans every year, and the nation's 30,000 assisted living facilities provided a home to about 800,000 elders during 2013-14. Residents in the 2 types of long-term care facilities are often socially isolated, physically and cognitively disabled, and diagnosed with multiple mental and medical conditions, all of which are associated with suicide in older adults. As a result, they may show elevated risk taking their own lives compared to community-living elders despite the fact that long-term care facilities are able to monitor closely the daily activities of their residents. However, the actual suicide risk among these residents is largely unknown. Moreover, it is expected that organizational characteristics and overall ability of long-term care facilities to provide safe and high quality of care play an important role in shaping the risk of suicide for residents at different points of care. However, empirical evidence on these questions is extremely limited. This study proposes to evaluate systematically the risk for suicide mortality among nursing home and assisted living residents, estimate suicide risk for nursing home residents at different points of care using longitudinal resident assessment records, and determine the associations of organizational characteristics and state policies with risk-adjusted suicide death rate. Organizational characteristics will be measured by nursing home structural factors (e.g. profit status), overall quality of care (e.g. measured by nurse staffing level), and organizational-level patient safety culture. This project will link the longitudinal National Violent Death Reporting System (NVDRS) and National Death Index (NDI) files to nursing home Minimum Data Sets (MDS) and Medicare beneficiary data, as well as other facility and state policy files. These data will be used to model resident risk for suicide death using cutting-edge statistical machine learning methods, quantify the variations in risk-adjusted suicide death rates at different levels (i.e. facility, region, and state levels), and test the hypotheses regarding organizational and policy impacts on suicide death. The information generated in this project will contribute to the knowledge of suicide burdens among the most vulnerable elders in need of residential long-term care. The knowledge gained will inform future organizational- and policy-level interventions to prevent suicide among elders receiving long-term care.

This project will develop a novel, automated technology to provide middle-school students and their teachers with real-time feedback about students? written explanations of physics phenomena. The use of evidence to build scientific explanations is a central practice by which scientific knowledge is generated and learned. Students often do not understand what a scientific explanation is and frequently write incomplete, non-causal accounts of scientific phenomenon. Teachers often have difficulties in helping students write explanations, as it is complex and time-consuming. Working in groups of three or four and experimenting with designing a roller coaster, students will learn about key principles in physics such as the conservation of energy and the laws concerning forces and motion. Each student will be provided with a digital journal. The prompts and information in the journal will structure the roller coaster activities (supported 6-8 weeks of instruction) and provide the students with a place to record their written ideas and explanations. At the close of several rounds of experimentation and analysis, students will write causal explanations for their current design. Through the use of the wise crowd automated assessment system, students will receive feedback on their writing. (The automated wise crowd model uses a content assessment of the explanations of experts as the foundation for analyzing and providing feedback to students.) Teachers will also use information from the system to facilitate full class discussions and individualized support. Project research and development activities will result in a fully developed and tested mechanism for providing feedback for students? science explanations. Through automated support of the content analysis of student writing across multiple assignments, the project innovations will allow teachers to more fully integrate writing into their assignments. Ultimately, the project can help students understand how scientific explanations are developed and justified and make them more critical consumers of scientific knowledge so they can make better informed decisions about scientific issues in everyday life.

The project will use a design-based research approach in developing the automated system using wise-crowd analysis and in assessing the impacts of the system on student learning and teacher classroom practices. Four research questions will guide the research: (1) How does feedback from the wise crowd system affect students? written explanation of scientific phenomenon?; (2) How do students with different levels of prior knowledge and reading comprehension benefit from automated feedback and teacher scaffolding?; (3) How do teachers use automated assessment and aggregated summaries of students? explanations during instruction?; and (4) In what ways does scaffolding from the wise crowd system and feedback from teachers support students? written explanations of learning? Through the four-year project, an iterative development process will include the design of the system and testing of two iterations of the system; research of student responses across the progression of roller coaster design and written assignments; and use of some validated and custom instruments to assess student understanding of key forces and assessment of student abilities to use data to evaluate claims. Classroom studies will use video data and researcher field notes to help understand how teachers facilitated the use of the wise-crowd system. Research will culminate in testing of the final version of the wise crowd system. Using a quasi-experimental design, classes will be randomly assigned to the treatment or comparison conditions. Findings will advance knowledge in the field about the best ways to integrate content assessment and feedback from the automated system with classroom and individual support from teachers to optimize learning for students. Materials and results generated from the project will be broadly disseminated, resulting in significant impacts for researchers and practitioners.

The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.