Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=abstract
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=abstract", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=abstract", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=abstract", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=abstract" }, "data": [ { "type": "Grant", "id": "8063", "attributes": { "award_id": "4U01DA053893-02", "title": "Wastewater Detection of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 1962927, "principal_investigator": { "id": 23959, "first_name": "Jeff", "last_name": "Wenzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "When faced with a pandemic such as SARS-Coronavirus-2 (SAR-CoV-2), the virus responsible for COVID-19, timely risk assessment and action are required to prevent public health impacts to entire communities. Because infected individuals may not have access to testing or may be asymptomatic and contraction can mean death, a proactive approach to detect the virus is needed to develop public health strategy to mitigate virus spread. Recent studies have detected SAR-CoV-2 genetic material in sewage and demonstrate a positive correlation between the concentration of viral markers and reported cases1-5. The Coronavirus Sewershed Surveillance Project (CSSP) is a collaborative effort to monitor sewersheds for genetic indicators of COVID-19 in wastewater to provide additional, population-level information about virus circulation that is not captured by clinical testing. Untreated wastewater (influent) samples are screened weekly from select sewersheds and targeted micro-sewersheds for detection and “true” prevalence. Congregate facilities provide unique opportunities for study because they are controlled populations where the precise number and timing of infections can be defined. Our team will utilize detailed monitoring of congregate facilities to define the precise per patient contribution and longevity of SARS-COV-2 RNA to wastewater by 1) increasing the number of facilities tested, 2) altering the frequency at which samples are collected, and 3) comparing sewershed data collected to clinical patient case data. Although SARS-COV-2 contribution/patient varies among communities, there have been clear outlier communities that produce little or no genetic material in the wastewater despite the presence of known outbreaks. The reason for this lost signal is not known, so our team will define factors that contribute to SARS- COV-2 signal suppression in wastewater by 1) defining the physical nature of the genetic material in the sewershed to better understand the types of factors that could suppress signal, 2) expanding testing within sewersheds with suppressed signal as well as from additional facilities with similar population and industry demographics as those with suppressed signal to narrow the sources of signal suppression, 3) performing exhaustive chemical characterization comparing wastewater from locations that are suppressed to those that are not to identify candidate compounds that could be causing suppression, and 4) obtaining or generating candidate inhibitors and test their ability to suppress signal from viral genetic material in a controlled experimental setting.", "keywords": [ "2019-nCoV", "Affect", "Biochemical", "Blood Circulation", "COVID-19", "COVID-19 detection", "COVID-19 patient", "COVID-19 testing", "Cessation of life", "Characteristics", "Chemicals", "Clinical", "Collection", "Communities", "Coronavirus", "Data", "Data Set", "Deposition", "Detection", "Disease Outbreaks", "Failure", "Frequencies", "Genetic", "Genetic Materials", "Health", "Immunology", "Individual", "Industry", "Infection", "Infrastructure", "Knowledge", "Laboratories", "Lead", "Location", "Longevity", "Longitudinal Studies", "Measurement", "Methods", "Microbiology", "Missouri", "Molecular", "Monitor", "Municipalities", "Natural Resources", "Nature", "Patients", "Population", "Prevalence", "Property", "Public Health", "RNA", "Recovery", "Reporting", "Research Personnel", "Risk Assessment", "SARS-CoV-2 infection", "Sampling", "Services", "Severities", "Sewage", "Signal Transduction", "Source", "Techniques", "Testing", "Time", "Universities", "Viral", "Viral Markers", "Virus", "demographics", "environmental transport", "exhaustion", "field study", "health record", "individual patient", "inhibitor/antagonist", "novel coronavirus", "pandemic disease", "prevent", "research clinical testing", "trend", "virus genetics", "wastewater samples", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "8064", "attributes": { "award_id": "1U01DA053893-01", "title": "Wastewater Detection of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 2000000, "principal_investigator": { "id": 23959, "first_name": "Jeff", "last_name": "Wenzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "When faced with a pandemic such as SARS-Coronavirus-2 (SAR-CoV-2), the virus responsible for COVID-19, timely risk assessment and action are required to prevent public health impacts to entire communities. Because infected individuals may not have access to testing or may be asymptomatic and contraction can mean death, a proactive approach to detect the virus is needed to develop public health strategy to mitigate virus spread. Recent studies have detected SAR-CoV-2 genetic material in sewage and demonstrate a positive correlation between the concentration of viral markers and reported cases1-5. The Coronavirus Sewershed Surveillance Project (CSSP) is a collaborative effort to monitor sewersheds for genetic indicators of COVID-19 in wastewater to provide additional, population-level information about virus circulation that is not captured by clinical testing. Untreated wastewater (influent) samples are screened weekly from select sewersheds and targeted micro-sewersheds for detection and “true” prevalence. Congregate facilities provide unique opportunities for study because they are controlled populations where the precise number and timing of infections can be defined. Our team will utilize detailed monitoring of congregate facilities to define the precise per patient contribution and longevity of SARS-COV-2 RNA to wastewater by 1) increasing the number of facilities tested, 2) altering the frequency at which samples are collected, and 3) comparing sewershed data collected to clinical patient case data. Although SARS-COV-2 contribution/patient varies among communities, there have been clear outlier communities that produce little or no genetic material in the wastewater despite the presence of known outbreaks. The reason for this lost signal is not known, so our team will define factors that contribute to SARS- COV-2 signal suppression in wastewater by 1) defining the physical nature of the genetic material in the sewershed to better understand the types of factors that could suppress signal, 2) expanding testing within sewersheds with suppressed signal as well as from additional facilities with similar population and industry demographics as those with suppressed signal to narrow the sources of signal suppression, 3) performing exhaustive chemical characterization comparing wastewater from locations that are suppressed to those that are not to identify candidate compounds that could be causing suppression, and 4) obtaining or generating candidate inhibitors and test their ability to suppress signal from viral genetic material in a controlled experimental setting.", "keywords": [ "2019-nCoV", "Affect", "Biochemical", "Blood Circulation", "COVID-19", "COVID-19 detection", "COVID-19 patient", "COVID-19 testing", "Cessation of life", "Characteristics", "Chemicals", "Clinical", "Collection", "Communities", "Coronavirus", "Data", "Data Set", "Deposition", "Detection", "Disease Outbreaks", "Failure", "Frequencies", "Genetic", "Genetic Materials", "Health", "Immunology", "Individual", "Industry", "Infection", "Infrastructure", "Knowledge", "Laboratories", "Lead", "Location", "Longevity", "Longitudinal Studies", "Measurement", "Methods", "Microbiology", "Missouri", "Molecular", "Monitor", "Municipalities", "Natural Resources", "Nature", "Patients", "Population", "Prevalence", "Property", "Public Health", "RNA", "Recovery", "Reporting", "Research Personnel", "Risk Assessment", "SARS-CoV-2 infection", "Sampling", "Services", "Severities", "Sewage", "Signal Transduction", "Source", "Techniques", "Testing", "Time", "Universities", "Viral", "Viral Markers", "Virus", "demographics", "environmental transport", "exhaustion", "field study", "health record", "individual patient", "inhibitor/antagonist", "novel coronavirus", "pandemic disease", "prevent", "research clinical testing", "trend", "virus genetics", "wastewater samples", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "14419", "attributes": { "award_id": "2101163", "title": "Education and Experience: Do Teacher Qualifications in Career-Focused STEM Courses Make a Difference?", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Discovery Research K-12" ], "program_reference_codes": [], "program_officials": [ { "id": 584, "first_name": "Robert", "last_name": "Ochsendorf", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 449669, "principal_investigator": { "id": 31032, "first_name": "David", "last_name": "Blazar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31032, "first_name": "David", "last_name": "Blazar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31033, "first_name": "Jay S", "last_name": "Plasman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "When high school students take “STEM-CTE” (i.e., career and technical education courses in science, technology, engineering, and mathematics fields), they have much stronger outcomes across the school-to-college/career pipeline, including lower dropout rates and better attendance in high school, stronger math achievement in 12th grade, and higher odds of pursuing advanced STEM courses in high school and college. Growing teacher research shows that teachers matter for students’ success, particularly in STEM. In particular, research has established that teacher education and credentials in STEM fields, as well as years of classroom teaching experiences are key teacher factors in supporting student outcomes. However, there has been limited prior research regarding (a) who teaches STEM-CTE courses and (b) whether the benefits of these courses and pathways are driven or influenced by specific characteristics of STEM-CTE teachers. This project will aim to explore these questions.<br/><br/>Using high school statewide longitudinal data from Maryland from 2012-2022, this study will first document who has taught STEM-CTE courses over this period. The dataset includes approximately 5,000 unique teacher observations and approximately 500,000 unique student observations. After exploring the teaching landscape, the study will then explore whether qualifications (i.e., education, credentials, teaching experience) of teachers in STEM-CTE high school courses were associated with their students’ success. Indicators of success in the dataset include end-of-course grades, STEM-CTE concentration/industry-recognized credentialing, advanced STEM coursetaking (e.g., honors, AP, IB, dual-enrollment), STEM standardized test scores, math SAT/ACT scores, attendance/suspension rates, on-time graduation, and reduced dropout. Data analysis includes multivariate regression analyses, supplemented with tests for nonrandom sorting of teachers to students.<br/><br/>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. <br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12464", "attributes": { "award_id": "2327799", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28403, "first_name": "Nicholas", "last_name": "Kortessis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1343, "ror": "https://ror.org/0207ad724", "name": "Wake Forest University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12488", "attributes": { "award_id": "2327797", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28425, "first_name": "Shengjie", "last_name": "Lai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12489", "attributes": { "award_id": "2327798", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28426, "first_name": "Robert", "last_name": "Holt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3064", "attributes": { "award_id": "1931511", "title": "Collaborative:Elements:RUI:Cyberinfrastructure for Pedestrian Dynamics-Based Analysis of Infection Propagation Through Air Travel", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software Institutes" ], "program_reference_codes": [], "program_officials": [ { "id": 9489, "first_name": "Rob", "last_name": "Beverly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-11-01", "end_date": "2022-10-31", "award_amount": 350000, "principal_investigator": { "id": 9491, "first_name": "Sikha", "last_name": "Bagui", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 705, "ror": "https://ror.org/002w4zy91", "name": "University of West Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 9490, "first_name": "Brian", "last_name": "Eddy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 705, "ror": "https://ror.org/002w4zy91", "name": "University of West Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "When people congregate - for example, at entertainment events, in crowds, and airplanes - they come into close contact with each other and can spread infectious diseases. The Disney World measles outbreak in 2016 is a prominent example. Air travel, in particular, is a leading factor in the spread of infections, and there have been several outbreaks of serious diseases that spread during air travel, such as SARS, H1N1 influenza, and tuberculosis. Public health policies and procedures for crowd management, boarding airplanes, etc. can help in mitigating the spread of disease, if these policies are science-based. The spread of directly transmitted diseases is governed by the movement patterns of people because the movement can bring an infected person close to others. The science of \"pedestrian dynamics\" provides mathematical models that can accurately simulate the movement of individuals in a crowd. These models allow scientists to understand how different policies, such as boarding procedures on planes, can prevent, or make worse, the transmission of infections. This project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. The project team is working closely with decision makers in airports, public health agencies, and the airline industry. This collaboration will lead to practical applications of this science that will improve public health. This project and the software will educate a wide range of scientists as well as students, in particular, students from under-represented groups, as well as professionals working in the public health fields.\n\nThis project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. Development of the proposed software will involve several innovations. It will include a novel phylogeography model that links fine-scale human movement data with virus genetic information to more accurately model geographic diffusion of viruses. New models for pedestrian movement will enable modeling of complex human movement patterns. A recommendation system for the choice of pedestrian dynamics models and a domain specific language for the input of policies and human behaviors will enhance usability by researchers in diverse fields. Community building initiatives will catalyze inter-disciplinary research to ensures the long-term sustainability of the project through a critical mass of contributors and users.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3075", "attributes": { "award_id": "1931483", "title": "Collaborative:Elements:Cyberinfrastructure for Pedestrian Dynamics-Based Analysis of Infection Propagation Through Air Travel", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software Institutes" ], "program_reference_codes": [], "program_officials": [ { "id": 9548, "first_name": "Rob", "last_name": "Beverly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-11-01", "end_date": "2022-10-31", "award_amount": 150000, "principal_investigator": { "id": 9549, "first_name": "Sirish", "last_name": "Namilae", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 315, "ror": "", "name": "Embry-Riddle Aeronautical University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "When people congregate - for example, at entertainment events, in crowds, and airplanes - they come into close contact with each other and can spread infectious diseases. The Disney World measles outbreak in 2016 is a prominent example. Air travel, in particular, is a leading factor in the spread of infections, and there have been several outbreaks of serious diseases that spread during air travel, such as SARS, H1N1 influenza, and tuberculosis. Public health policies and procedures for crowd management, boarding airplanes, etc. can help in mitigating the spread of disease, if these policies are science-based. The spread of directly transmitted diseases is governed by the movement patterns of people because the movement can bring an infected person close to others. The science of \"pedestrian dynamics\" provides mathematical models that can accurately simulate the movement of individuals in a crowd. These models allow scientists to understand how different policies, such as boarding procedures on planes, can prevent, or make worse, the transmission of infections. This project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. The project team is working closely with decision makers in airports, public health agencies, and the airline industry. This collaboration will lead to practical applications of this science that will improve public health. This project and the software will educate a wide range of scientists as well as students, in particular, students from under-represented groups, as well as professionals working in the public health fields.\n\nThis project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. Development of the proposed software will involve several innovations. It will include a novel phylogeography model that links fine-scale human movement data with virus genetic information to more accurately model geographic diffusion of viruses. New models for pedestrian movement will enable modeling of complex human movement patterns. A recommendation system for the choice of pedestrian dynamics models and a domain specific language for the input of policies and human behaviors will enhance usability by researchers in diverse fields. Community building initiatives will catalyze inter-disciplinary research to ensures the long-term sustainability of the project through a critical mass of contributors and users.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12236", "attributes": { "award_id": "1R01NR020886-01", "title": "Communication quality during family meetings in the intensive care unit: how does quality impact health outcomes?", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Nursing Research (NINR)" ], "program_reference_codes": [], "program_officials": [ { "id": 6036, "first_name": "Karen", "last_name": "Kehl", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-16", "end_date": "2028-05-31", "award_amount": 655264, "principal_investigator": { "id": 28109, "first_name": "Allison M", "last_name": "Scott", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 28110, "first_name": "Lauren Jodi", "last_name": "Van Scoy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1103, "ror": "", "name": "PENNSYLVANIA STATE UNIV HERSHEY MED CTR", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "When poor communication between clinicians and family members of ICU patients, disparities related to social determinants of health (SDOH), and a heightened distrust of healthcare professionals all coalesced during the COVID-19 pandemic, “the perfect storm” emerged. Improving communication and trust in healthcare professionals is critical in high-stakes ICU environments where the need for shared decision-making demands that clinicians rapidly establish rapport and therapeutic alliance with family members of non-capacitated patients. Many well-designed trials testing ICU communication interventions have had negative or minimally impactful results, perhaps in part because we have only a rudimentary understanding of how SDOH impact communication in the ICU, a clinical context where underrepresented individuals also suffer from disparities. The goals of this R01 are to conduct a mediation analysis that will: 1) examine SDOH’s impact on communication between family members and ICU clinicians; 2) identify mechanisms of action related to how communication quality affects distrust of healthcare professionals and therapeutic alliance; and 3) determine how these factors contribute to or mediate outcomes for family members of ICU patients. This longitudinal, observational study will collect data from 320 family members from 5 ICUs serving underrepresented rural, Black, and Hispanic communities in 3 states. Our primary health outcome is the provision of family-centered care (as measured by the Patient Perceptions of Patient Centeredness questionnaire- Family Version). Secondary health outcomes include family members’ psychological stress (anxiety, depression, symptoms of post-traumatic stress disorder) and measures of patient ICU utilization (e.g., ICU LOS, ventilator days). Mediating factors to be examined include communication quality, healthcare distrust, and therapeutic alliance. Moderating factors include family members’ intrinsic traits (e.g., personality traits and decision-making style). We hypothesize that: 1) poor SDOH yield poor outcomes and result in less attention to family-centered care and worse ICU patient utilization outcomes; and 2) higher communication quality will improve the therapeutic relationship and healthcare trust and result in improved attention to family-centered care and improved ICU patient utilization outcomes among all patients regardless of SDOH. Using our results, we will adapt a prominent conceptual model of communication to address the high-stakes communication needs of families from underserved communities. Completing this work will advance the field by providing data to allow new understanding of how SDOH and other factors (e.g., communication quality, trust) relate to provision of patient- and family-centered care in the post-pandemic context. The knowledge gained will inform new content and concrete communication strategies for future ICU interventions aiming to facilitate high-quality communication, help to restore trust in healthcare, and improve therapeutic alliances in pursuit of achieving patient- and family- centered ICU care.", "keywords": [ "Address", "Affect", "Anxiety", "Attention", "Black race", "COVID-19 pandemic", "Caring", "Clinical", "Communication", "Data", "Decision Making", "Disparity", "Economic Conditions", "Economics", "Education", "Environment", "Family", "Family member", "Future", "Goals", "Health", "Health Professional", "Health education", "Healthcare", "Individual", "Intensive Care Units", "Intervention", "Intrinsic factor", "Knowledge", "Length of Stay", "Longitudinal observational study", "Measures", "Mediating", "Mediation", "Mental Depression", "Modeling", "Neighborhoods", "Outcome", "Pathway interactions", "Patient-Focused Outcomes", "Patients", "Perception", "Personality Traits", "Post-Traumatic Stress Disorders", "Protocols documentation", "Psychological Stress", "Questionnaires", "Research", "Risk", "Rural", "Rural Community", "Severity of illness", "Social Conditions", "Stress", "Testing", "Therapeutic", "Trust", "Underrepresented Populations", "Ventilator", "Work", "demographics", "design", "distrust", "experience", "health care availability", "hispanic community", "improved", "innovation", "meetings", "outcome disparities", "pandemic disease", "patient oriented", "post-pandemic", "post-traumatic symptoms", "programs", "psychological outcomes", "rural underrepresented", "shared decision making", "social health determinants", "trait", "trial design", "underserved community" ], "approved": true } }, { "type": "Grant", "id": "14951", "attributes": { "award_id": "5R01NR020886-02", "title": "Communication quality during family meetings in the intensive care unit: how does quality impact health outcomes?", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Nursing Research (NINR)" ], "program_reference_codes": [], "program_officials": [ { "id": 6036, "first_name": "Karen", "last_name": "Kehl", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-16", "end_date": "2028-05-31", "award_amount": 590414, "principal_investigator": { "id": 28109, "first_name": "Allison M", "last_name": "Scott", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 28110, "first_name": "Lauren Jodi", "last_name": "Van Scoy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1103, "ror": "", "name": "PENNSYLVANIA STATE UNIV HERSHEY MED CTR", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "When poor communication between clinicians and family members of ICU patients, disparities related to social determinants of health (SDOH), and a heightened distrust of healthcare professionals all coalesced during the COVID-19 pandemic, “the perfect storm” emerged. Improving communication and trust in healthcare professionals is critical in high-stakes ICU environments where the need for shared decision-making demands that clinicians rapidly establish rapport and therapeutic alliance with family members of non-capacitated patients. Many well-designed trials testing ICU communication interventions have had negative or minimally impactful results, perhaps in part because we have only a rudimentary understanding of how SDOH impact communication in the ICU, a clinical context where underrepresented individuals also suffer from disparities. The goals of this R01 are to conduct a mediation analysis that will: 1) examine SDOH’s impact on communication between family members and ICU clinicians; 2) identify mechanisms of action related to how communication quality affects distrust of healthcare professionals and therapeutic alliance; and 3) determine how these factors contribute to or mediate outcomes for family members of ICU patients. This longitudinal, observational study will collect data from 320 family members from 5 ICUs serving underrepresented rural, Black, and Hispanic communities in 3 states. Our primary health outcome is the provision of family-centered care (as measured by the Patient Perceptions of Patient Centeredness questionnaire- Family Version). Secondary health outcomes include family members’ psychological stress (anxiety, depression, symptoms of post-traumatic stress disorder) and measures of patient ICU utilization (e.g., ICU LOS, ventilator days). Mediating factors to be examined include communication quality, healthcare distrust, and therapeutic alliance. Moderating factors include family members’ intrinsic traits (e.g., personality traits and decision-making style). We hypothesize that: 1) poor SDOH yield poor outcomes and result in less attention to family-centered care and worse ICU patient utilization outcomes; and 2) higher communication quality will improve the therapeutic relationship and healthcare trust and result in improved attention to family-centered care and improved ICU patient utilization outcomes among all patients regardless of SDOH. Using our results, we will adapt a prominent conceptual model of communication to address the high-stakes communication needs of families from underserved communities. Completing this work will advance the field by providing data to allow new understanding of how SDOH and other factors (e.g., communication quality, trust) relate to provision of patient- and family-centered care in the post-pandemic context. The knowledge gained will inform new content and concrete communication strategies for future ICU interventions aiming to facilitate high-quality communication, help to restore trust in healthcare, and improve therapeutic alliances in pursuit of achieving patient- and family- centered ICU care.", "keywords": [ "Address", "Affect", "Anxiety", "Attention", "Black race", "COVID-19 pandemic", "Caring", "Clinical", "Communication", "Data", "Decision Making", "Disparity", "Economic Conditions", "Economics", "Education", "Environment", "Family", "Family member", "Future", "Goals", "Health", "Health Professional", "Health education", "Healthcare", "Individual", "Intensive Care Units", "Intervention", "Intrinsic factor", "Knowledge", "Length of Stay", "Longitudinal observational study", "Measures", "Mediating", "Mediation", "Mental Depression", "Modeling", "Neighborhoods", "Outcome", "Pathway interactions", "Patient-Focused Outcomes", "Patients", "Perception", "Personality Traits", "Post-Traumatic Stress Disorders", "Protocols documentation", "Psychological Stress", "Questionnaires", "Research", "Risk", "Rural", "Rural Community", "Severity of illness", "Social Conditions", "Stress", "Testing", "Therapeutic", "Trust", "Underrepresented Populations", "Ventilator", "Work", "demographics", "design", "distrust", "experience", "health care availability", "hispanic community", "improved", "innovation", "meetings", "outcome disparities", "pandemic disease", "patient oriented", "post-pandemic", "post-traumatic symptoms", "programs", "psychological outcomes", "rural underrepresented", "shared decision making", "social health determinants", "trait", "trial design", "underserved community" ], "approved": true } } ], "meta": { "pagination": { "page": 1392, "pages": 1424, "count": 14236 } } }