Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=program_officials
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=program_officials", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=program_officials", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=program_officials", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=program_officials" }, "data": [ { "type": "Grant", "id": "15664", "attributes": { "award_id": "2438012", "title": "I-Corps: Translation Potential of an Enhanced Fluorescence-based Diagnostic Technology for the Detection of Lyme Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 31316, "first_name": "Jaime A.", "last_name": "Camelio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-12-01", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32172, "first_name": "Nathaniel", "last_name": "Cady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 571, "ror": "", "name": "SUNY at Albany", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of an enhanced fluorescence-based diagnostic technology for the detection of antibodies and other biomarkers for disease diagnostics. The base technology has been demonstrated for diagnosing high profile diseases including COVID-19 and Lyme disease. For this I-Corps effort, Lyme disease has been chosen as the beachhead market due to the current diagnostic challenges, and the growing market for fast and accurate Lyme disease diagnostic technologies. The accepted standard for Lyme disease, known as standard two-tiered testing (STTT) is time consuming, requires specialists to run, and can be unreliable, especially for early stages of the disease. This technology has proven to alleviate these pain points, providing rapid and accurate Lyme disease diagnosis, especially for early Lyme disease patients. The platform has also been utilized for detecting RNA-protein and DNA-protein interactions, which potentially broadens its utility for a large number of different disease diagnostic applications, biomarker discovery, and biological / pharmaceutical research applications. The technology may have impact in several clinical and biological research fields. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a photonics-based Lyme disease diagnostic platform. Lyme disease is the most common vector-borne disease in the United States and, despite advances, remains a considerable diagnostic challenge. Confirmatory diagnosis requires a second test, performed in series, often in batches, and at a centralized laboratory. This delay can lead to considerable morbidity in disseminated Lyme disease. This technology is a low-cost, highly sensitive, fluorescence-based platform, which provides a rapid, easy-to-use, and highly accurate Lyme test that could be used outside traditional clinical laboratories or for more rapid and accurate diagnosis within clinical laboratories. The proof-of-principle research positions the technology as a rapid (<40 minutes total test time) and reliable alternative to traditional Lyme tests, while retaining the full sophistication of a two-tiered testing system. 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": "15681", "attributes": { "award_id": "2524663", "title": "I-Corps: Translation Potential of Voice Analysis to Pre-screen Airborne Diseases", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 31316, "first_name": "Jaime A.", "last_name": "Camelio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-05-15", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32527, "first_name": "Andres", "last_name": "Valdez", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This I-Corps project focuses on the development of a non-invasive digital health solution that uses voice and biometric signals collected from smart devices to pre-screen for respiratory illnesses such as respiratory syncytial virus (RSV), influenza, and COVID-19. The technology addresses a growing national health concern: the delayed detection and spread of airborne diseases, which strain healthcare systems, reduce workplace productivity, and threaten public health—particularly in crowded or high-risk environments like schools, airports, and hospitals. The solution aims to empower individuals with early warning tools, allowing them to take preventative action before symptoms worsen or spread to others. By minimizing unnecessary clinic visits, enabling quicker triage, and supporting population-level monitoring, this technology promotes national health and welfare while contributing to more resilient and responsive healthcare infrastructures. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a voice-enabled biometric monitoring system powered by machine learning algorithms that analyze deviations from a user's baseline in real time. The system integrates voice modulations, heart rate, and temperature data, and correlates them with clinically observed patterns of respiratory illness. Recent advances in mobile computing, edge artificial intelligence (AI), and signal processing enable the secure and scalable deployment of this solution across smartphones, wearables, and smart speakers. Unlike traditional diagnostics, this technology offers a passive and continuous approach to health surveillance, benefiting users through earlier detection, reduced costs, and improved public health coordination. 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": "14632", "attributes": { "award_id": "2344234", "title": "HBCU-UP RAPID: HBCU Leadership Crisis on STEM Broadening Participation and Research Capacity Building - Impact and Implications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Hist Black Colleges and Univ" ], "program_reference_codes": [], "program_officials": [ { "id": 31325, "first_name": "Joyce", "last_name": "Belcher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-04-01", "end_date": null, "award_amount": 199999, "principal_investigator": { "id": 4514, "first_name": "Trina", "last_name": "Fletcher", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) supports RAPID projects when there is an urgency concerning the availability of, or access to, data, facilities, or specialized equipment, including quick-response research on natural or anthropogenic disasters and similar unanticipated events, such as the COVID-19 pandemic. During and after the COVID-19 pandemic, several higher education institutions experienced changes in the president and chancellor positions. For Historically Black Colleges and Universities (HBCUs), in 2022 alone, there were 23 leadership changes announced, and in 2023, 41 changes were announced, almost double within one year. Essentially, one in four HBCUs experienced a resignation or termination at the highest administrative level. These leadership changes have been an added challenge to the ongoing recovery efforts of many HBCUs that were also disproportionately impacted by the global pandemic. HBCUs are critical for science, technology, engineering, and mathematics (STEM) education and workforce development and for their contributions to STEM research. HBCUs are critical players in helping the nation stay competitive globally and are a national asset, considering the large numbers of diverse students earning degrees in STEM from HBCUs. Unfortunately, excessive executive leadership turnover could negatively impact those efforts.<br/><br/>This research study will explore the institutional impact of turnover at the President/Chancellor and executive cabinet levels at HBCUs. By using pilot data collected at one of the largest annual convenings of HBCU executives related to the impact and implications of HBCU leadership turnover, our proposed convening to collect rich qualitative data, and their feedback on the pilot survey results, will dynamically and strategically gain insight on this unprecedented challenge. This project will contribute to better understanding the impacts of leadership turnover and create recommendations for best practices. Ultimately, the results from this study are intended to increase stability at HBCUs experiencing leadership transitions so HBCUs can continue to play their important role in broadening participation in STEM, undertaking important STEM research, and providing excellent STEM educational programs.<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": "14660", "attributes": { "award_id": "1R03DK139283-01", "title": "Innovating acute care for liver disease through a pilot, patient-centric Hospital@Home program", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)" ], "program_reference_codes": [], "program_officials": [ { "id": 31357, "first_name": "KATRINA SHUEH WEN", "last_name": "Loh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-04-01", "end_date": "2026-03-31", "award_amount": 129301, "principal_investigator": { "id": 31358, "first_name": "Archita P.", "last_name": "Desai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1621, "ror": "", "name": "INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Chronic liver disease (CLD) is a top-ten leading cause of death in Americans, aged 25-64, and ethnic minorities. Individuals with CLD suffer from lower health-related quality of life (HRQOL), disability and place a burden on their family caregivers. As individuals progress to end-stage liver disease, health-care utilization (HCU) increases exponentially, largely due to inpatient care for acute decompensations. In certain clinical scenarios, after the initial 24-48 hour period, the remaining hospital days are often used to meet lower acuity needs such as continued administration of IV medications, titration of medications and/or daily lab monitoring prior to transition to outpatient care. This latter period reflects an opportunity to improve HCU in CLD patients by addressing unmet needs within the home environment, reducing hospitalization-related complications, refining the transition to outpatient care, and increasing the healthcare system’s capacity by shortening the length of initial hospitalizations and reducing rehospitalizations. Hospital at Home (H@H) is an emerging model of home-based care, designed to extend traditional, inpatient hospital care which may address these needs. Through H@H, acute medical care services as well as ancillary care such as rehabilitation therapy can be delivered in the home. Prior studies have reported success with implementing this model in terms of clinical and economic efficacy as well as feasibility. Importantly, the model demonstrated greater satisfaction with care from patients, their family members, and their providers. Since November 2020, Centers for Medicare and Medicaid Services has provided a waiver for H@H services in response to the public health emergency resulting in a sudden rise in the prevalence of H@H programs around the country. As an early adopter, Indiana University Health (IUH) introduced a H@H program to increase hospital capacity. The model at IUH successfully managed patients with moderate COVID-19 infection, leading to an expansion in 2022 to include management of common infections and heart failure. As a next step in expansion, the IUH H@H team is partnering with PI and the IUH Hepatology team to manage select patients with CLD. The overall goal of this proposal is to assess whether IUH’s H@H program represents a novel care delivery model in cirrhosis that is safe, improves patient and caregiver experience as well as reduces HCU in the high-risk, CLD population. To achieve this goal, we propose a pilot, prospective observational study of 30 individuals who receive care for their liver disease-related admission through IUH’s H@H program with two specific aims. Specific Aim 1 is to assess the (1) feasibility, (2) safety, (3) patient reported outcomes and (4) impact on healthcare utilization associated with expanding IUH’s H@H program in the CLD population. Specific Aim 2 is to determine patient and caregiver acceptability of completing care of acute exacerbations of CLD using IUH’s H@H program and elicit patient- and caregiver-centric revisions of the H@H program using human-centered design methodology.", "keywords": [ "Acute", "Address", "Admission activity", "Ambulatory Care", "American", "Caregiver Burden", "Caregivers", "Caring", "Cause of Death", "Cirrhosis", "Client satisfaction", "Clinical", "Collection", "Country", "Data Collection", "Economics", "Enrollment", "Ensure", "Family Caregiver", "Family member", "Feedback", "Foundations", "Future", "Goals", "Health", "Healthcare Systems", "Heart failure", "Hepatology", "Home", "Home environment", "Hospital Design", "Hospitalization", "Hospitals", "Hour", "Indiana", "Individual", "Infection", "Inpatients", "Interview", "Length", "Length of Stay", "Liver diseases", "Measurement", "Measures", "Medical", "Methodology", "Modeling", "Monitor", "Observational Study", "Participant", "Patient Outcomes Assessments", "Patient Selection", "Patients", "Pharmaceutical Preparations", "Pilot Projects", "Population", "Prevalence", "Procedures", "Prospective Studies", "Provider", "Quality of life", "Randomized Controlled Trials", "Rehabilitation therapy", "Reporting", "Research Design", "SARS-CoV-2 infection", "Safety", "Science", "Service delivery model", "Services", "Solid", "Structure", "Technology", "Titrations", "United States Centers for Medicare and Medicaid Services", "Universities", "Visit", "acute care", "aged", "ancillary care", "care systems", "chronic liver disease", "design", "disability", "efficacy trial", "end stage liver disease", "ethnic minority", "experience", "follow-up", "health care delivery", "health care service utilization", "health related quality of life", "healthcare burden", "high risk", "high risk population", "hospital care", "hospital readmission", "human centered design", "improved", "innovation", "inpatient service", "meetings", "mortality", "novel", "patient home care", "patient oriented", "programs", "prospective", "public health emergency", "recruit", "response", "satisfaction", "success", "waiver" ], "approved": true } }, { "type": "Grant", "id": "14687", "attributes": { "award_id": "1R01CA289295-01", "title": "Strategies for Reaching and Impacting Our Communities Sustainably (NWP-ROCS Program)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [ { "id": 31381, "first_name": "ELIZABETH ANNE", "last_name": "Sarma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-04-01", "end_date": "2029-03-31", "award_amount": 542964, "principal_investigator": { "id": 31382, "first_name": "Rachel C", "last_name": "Shelton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 781, "ror": "", "name": "COLUMBIA UNIVERSITY HEALTH SCIENCES", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Widespread implementation, scale-up, and sustainability of culturally-appropriate, evidence-based programs is critical to reducing the significant and disproportionate burden of cancer among Black women. Community- engaged Lay Health Advisor (LHA) programs are highly successful in reducing health inequities for cancer. One of the most robust evidence-based and nationally disseminated LHA cancer prevention/screening programs is The National Witness Project (NWP). NWP is one of the few equity-focused LHA programs with both longevity and evidence of impact in Black communities nationally. Despite its impact, NWP continues to face challenges to sustainability and the long-term delivery of the program. Advancing the science of sustainability is urgent, as inequities in cancer and challenges to the sustainability of evidence-based programs were exacerbated during the COVID pandemic. Research is critically needed on sustainability, particularly among low-resource settings and communities that face historical and ongoing structural and systemic barriers to health, to make progress towards racial equity for cancer screening and outcomes. Our team is uniquely poised to lead and advance research in this area. Building off of our work on sustainability and a long-term partnership with NWP, we propose a national mixed-methods prospective study with the following aims. First, in aim 1 we will refine, with a sub-sample of NWP sites nationally (n=6), a package of sustainability strategies to support the ongoing delivery of NWP at scale, with the long-term goal of addressing cancer screening inequities among Black women. We will focus on strategies for building capacity and partnerships to enhance sustained impact and delivery of NWP and support retention of LHAs (e.g. novel curricula/training; tailored technical assistance; Community of Practice model to share lessons across sites), with the goal of enhancing capacity for: 1) building partnerships/identifying champions at academic/healthcare centers to leverage organizational resources; 2) building business case for program’s value; 3) adapting to local community needs and context. In Aim 2, we propose to deliver and examine the impact of this refined package of sustainability strategies on multiple sustainability outcomes annually over four years across 16 NWP sites nationally using a pre-post cluster prospective design. Finally, in aim 3, we plan to apply a concurrent mixed-methods approach (n=200 surveys and 50-65 in-depth interviews) to examine the uptake, acceptability, appropriateness, feasibility and impact of sustainability strategies among 16 NWP sites to explore the processes through which strategies build capacity for and influence sustainability of equity-focused EBIs in community settings over the 4 years. This research is timely, providing a key opportunity to advance scientific understanding of strategies to promote sustainability among a generalizable, nationally disseminated program. Findings lay the groundwork for enhancing sustainability of trusted community-led programs and making progress towards racial equity in cancer and cancer screening.", "keywords": [ "Accountability", "Address", "Area", "Behavior", "Black race", "Breast", "Businesses", "COVID-19 pandemic", "Cancer Burden", "Cancer Center", "Cancer Control", "Cancer Model", "Cancer Survivor", "Cervical Cancer Screening", "Collaborations", "Communities", "Community of Practice", "Diagnostic", "Discrimination", "Early Diagnosis", "Education", "Educational Curriculum", "Entrepreneurship", "Equity", "Evaluation", "Evidence based program", "Face", "Goals", "Health", "Healthcare", "Inequity", "Infrastructure", "Institution", "Institutionalization", "Interview", "Knowledge", "Lead", "Longevity", "Malignant Neoplasms", "Malignant neoplasm of cervix uteri", "Medical", "Methods", "Modeling", "National Cancer Institute", "Outcome", "Participant", "Play", "Process", "Prospective Studies", "Racial Equity", "Research", "Resource-limited setting", "Resources", "Science", "Scientific Advances and Accomplishments", "Screening for cancer", "Services", "Site", "Surveys", "Testing", "Time", "Training", "Trust", "Underserved Population", "Woman", "Work", "black women", "cancer prevention", "community partnership", "community setting", "design", "evidence base", "experience", "follow-up", "health equity", "health inequalities", "implementation science", "improved", "novel", "programs", "prospective", "racial disparity", "scale up", "screening program", "skills", "social", "sustainability framework", "systemic barrier", "theories", "uptake" ], "approved": true } }, { "type": "Grant", "id": "14695", "attributes": { "award_id": "1R41AA031393-01", "title": "Development of a mobile harm reduction treatment for alcohol use disorder (mHaRT-A)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Alcohol Abuse and Alcoholism (NIAAA)" ], "program_reference_codes": [], "program_officials": [ { "id": 31390, "first_name": "DAMIYA EVE", "last_name": "Whitaker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-03-01", "end_date": "2025-02-28", "award_amount": 369597, "principal_investigator": { "id": 31391, "first_name": "Susan E", "last_name": "Collins", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2490, "ror": "", "name": "HART3S - A SOCIAL PURPOSE CORPORATION", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Since the COVID-19 pandemic, deaths due to alcohol use spiked to 25% percent beyond its expected toll. It is this ever-growing global burden of alcohol-related harm that has propelled our team to conduct over 15 years of research to codevelop with people with AUD and then scientifically test a highly flexible, patient-driven, low- barrier, in-person treatment known as harm-reduction treatment for alcohol use disorder (HaRT-A). HaRT-A meets people where they are at to help them create goals around reducing alcohol-related harm and engage safer-drinking strategies, even if they are not ready, willing or able to stop drinking. Findings from our recent, NIH-funded randomized clinical trials have shown the efficacy of this low-barrier, in-person HaRT-A in reducing heavy alcohol use and alcohol-related harm and in improving health-related quality of life, even in a population that is socially marginalized and severely impacted by AUD. However, the vast majority of people with AUD -- approximately 93% in 2021 -- do not seek in-person treatment. Barriers to alcohol treatment attendance include the high time and monetary cost of in-person treatment, its lack of patient-centeredness and flexibility, and the stigma of others knowing one is in treatment. In addition, the pandemic made greater swaths of the general public comfortable with digital technologies for health and treatment service provision. Thus, a mobile application of HaRT-A could reduce barriers to help-seeking by creating easier access to evidence-based alcohol interventions. In this STTR phase 1 application, we propose to develop and initially test a mobile application to deliver the HaRT-A components (mHaRT-A) and thus address alcohol-related harm and AUD in a more accessible and scalable as well as less costly and stigmatizing way. We plan to accomplish this goal by building the mHaRT-A prototype with a human-centered design framework and then iteratively testing its usability, feasibility and acceptability among people with AUD. Our Aim 1 milestone is a human-centered prototype of the mHaRT-A progressive web application that will advance for usability testing in Aim 2. Our Aim 2 milestone is to complete lab-based usability testing of the mHaRT-A and achieve a System Usability Scale score of 70 or greater, which would indicate an adequate system. Our Aim 3 milestone is to test the feasibility and acceptability of the high-fidelity mHaRT-a prototype and the planned STTR Phase 2 randomized clinical trial procedures as demonstrated via adequate recruitment, retention and implementation of the planned procedures. Successful completion of the STTR Phase 1 project will create the foundation for a well-powered STTR Phase 2 randomized clinical trial testing mHaRT-A’s efficacy in reducing alcohol-related harm and improving health-related quality of life. If successful, this overarching project will build an evidence base for the successful commercialization and dissemination of mHaRT-A to people with AUD and to healthcare providers and systems.", "keywords": [ "Abstinence", "Address", "Alcohol consumption", "Alcohols", "COVID-19 pandemic", "Cessation of life", "Data", "Development", "Disease remission", "Drug Controls", "Drug usage", "Feedback", "Financial cost", "Foundations", "Funding", "General Population", "Goals", "Harm Reduction", "Health", "Health Personnel", "Health Services", "Health Technology", "Healthcare Systems", "Heavy Drinking", "Human", "Individual", "Intervention", "Interview", "Laboratories", "Marketing", "Meta-Analysis", "Methods", "Morbidity - disease rate", "National Institute on Alcohol Abuse and Alcoholism", "Participant", "Patients", "Persons", "Phase", "Population", "Positioning Attribute", "Procedures", "Public Health", "Quality of life", "Recovery", "Reporting", "Research", "Research Design", "Research Personnel", "Service provision", "Small Business Technology Transfer Research", "Stigmatization", "Surveys", "Symptoms", "System", "Testing", "Time", "United States National Institutes of Health", "acceptability and feasibility", "alcohol abstinence", "alcohol abuse therapy", "alcohol availability", "alcohol intervention", "alcohol use disorder", "alcohol-related death", "barrier to care", "biopsychosocial", "care systems", "clinical care", "commercialization", "cost", "design", "digital technology", "drinking", "evidence base", "experience", "feasibility testing", "field study", "flexibility", "health related quality of life", "help-seeking behavior", "human centered design", "improved", "innovation", "mHealth", "marginalization", "mobile app development", "mobile application", "pandemic disease", "patient oriented", "preference", "prototype", "public health relevance", "randomized clinical trials", "recruit", "reduced alcohol use", "response", "social", "social stigma", "substance use treatment", "treatment services", "usability", "web app" ], "approved": true } }, { "type": "Grant", "id": "14697", "attributes": { "award_id": "1R03TR004976-01", "title": "The Interaction of Public Health Emergencies: Understanding Nation-wide and City-wide Spatiotemporal Dynamics of COVID-19 Transmission in a Warming World", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [ { "id": 31393, "first_name": "Patrick", "last_name": "Brown", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-03-01", "end_date": "2026-02-28", "award_amount": 84750, "principal_investigator": { "id": 31394, "first_name": "Arnab Kumar", "last_name": "Ghosh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 825, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Program Summary I am an early-stage investigator and recent KL2 scholar who has used econometric and spatiotemporal meth- ods to examine the propagation of C-19 in vulnerable populations. The focus of this R03 is to build on this work to examine the interaction between two devastating public health emergencies: the COVID-19 (C-19) pan- demic and climate change-amplified extreme heat events (EHEs). Although coronaviruses in general survive longer in environments of lower humidity, temperature, and sunlight, C-19 propagation has surged in summer months. A proposed explanation is that SARS-COV2 remains stable in hotter, humid environments, and that C- 19 transmission is promoted by heat-avoidant behavior that increases indoor physical proximity and air condi- tioner use. EHEs adversely affected one in five Americans. Thus, EHEs may intensify C-19 propagation, partic- ularly among more individuals and subpopulations vulnerable to both C-19-related and EHE-related morbidity and mortality – older aged individuals with medical comorbidities, socioeconomically disadvantaged individu- als, and minorities. An examination of this interaction will provide the first evidence of the association between climate-amplified EHEs and C-19, providing important data for future pandemic preparedness and climate-am- plified infectious disease propagation – a critical area of inquiry as described by several institutions, including the Federal Government. This proposal’s central objective is to examine the relationship between EHEs and C- 19 propagation, providing data that can subsequently be translated into future tools for pandemic prepared- ness in the age of climate change. My central hypothesis is that EHEs increase C-19 risk by increasing house- bound populations and promoting SARS-COV2 transmission dynamics, particularly in areas with higher pro- portions of older aged individuals, racial/ethnic minorities, and other socioeconomically disadvantaged individu- als. I will test this hypothesis by employing multivariable spatiotemporal models with quasi-experimental de- signs on several secondary data sources from the Johns Hopkins Coronavirus Resource Center (daily, nation- wide, county-level C-19 outcomes) and patient-level data from the NCATS National COVID Cohort Collabora- tive (N3C), combined with area-level socioeconomic data from the US census, and environmental data from the National Weather Service (NWS) from 2020-2022. The aims are: 1) To examine the association between EHEs and county-wide C-19 risk from a national perspective; and 2) Identify adult individual-level demo- graphic, clinical, and area-level socioeconomic characteristics associated with increased risk of C-19 related hospitalization after EHEs. This work is highly relevant to the National Institutes of Health (NIH) Strategic Framework for Climate Change and Health Initiative. In addition to supporting my pathway to independence as a translational physician-scientist, findings from this proposal will lay the foundations of future work focused on infectious disease epidemiology and climate change in a future R01 submission to the NIEHS or NIAID. 1", "keywords": [ "2019-nCoV", "Accounting", "Admission activity", "Adult", "Adverse event", "Affect", "Age", "Air", "American", "Area", "Award", "Behavior", "COVID-19", "COVID-19 morbidity", "COVID-19 pandemic", "COVID-19 risk", "Censuses", "Cessation of life", "Characteristics", "Chronic Disease", "Cities", "Climate", "Clinical", "Communicable Diseases", "Coronavirus", "County", "Crowding", "Data", "Data Sources", "Economically Deprived Population", "Environment", "Event", "Federal Government", "Foundations", "Frequencies", "Future", "Health", "Hospitalization", "Household", "Humidity", "Individual", "Infectious Disease Epidemiology", "Institution", "Intervention", "Measures", "Medical", "Methods", "Minority", "Minority Groups", "Modeling", "Morbidity - disease rate", "National Center for Advancing Translational Sciences", "National Heart Lung and Blood Institute", "National Institute of Allergy and Infectious Disease", "National Institute of Environmental Health Sciences", "Outcome", "Pathway interactions", "Patients", "Physiological", "Population", "Populations at Risk", "Poverty", "Prevalence", "Public Health", "Quasi-experiment", "Research", "Research Personnel", "Resources", "Risk", "SARS-CoV-2 transmission", "Scientist", "Services", "Severities", "Southwestern United States", "Specific qualifier value", "Sunlight", "Temperature", "Testing", "Translating", "United States National Institutes of Health", "Vaccination", "Vulnerable Populations", "Weather", "Work", "climate change", "climate-related health", "cohort", "comorbidity", "design", "econometrics", "ethnic minority", "evidence base", "experience", "extreme heat", "extreme weather", "future pandemic", "health equity", "human old age (65+)", "improved", "indexing", "mortality", "pandemic preparedness", "programs", "public health emergency", "racial minority", "simulation", "socioeconomic disadvantage", "socioeconomics", "spatiotemporal", "theories", "tool", "translational physician", "viral pandemic" ], "approved": true } }, { "type": "Grant", "id": "14705", "attributes": { "award_id": "1R03TR004608-01A1", "title": "The Translational Efficiency of Linking Family and Household Members to Study and Intervene on the Ripple Effects of Trauma", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [ { "id": 31393, "first_name": "Patrick", "last_name": "Brown", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-03-06", "end_date": "2026-02-28", "award_amount": 79250, "principal_investigator": { "id": 31400, "first_name": "Lauren A", "last_name": "Magee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1621, "ror": "", "name": "INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Rates of firearm injuries reached unprecedented levels during the COVID-19 pandemic, disproportionally represented women and children, and rates remain at historical high levels in 2023. Exposure to firearm injury is associated with increased adverse health outcomes and mental health needs, further compounding existing health and racial inequities. Prior research has focused on health outcomes and interventions at the individual level and community level, with less attention focused on family and household member’s health outcomes due to both methodological challenges and data limitations. Therefore, current health interventions may miss the “ripple effect” of traumatic health events. The lack of methods linking family and household members is a significant roadblock for clinical and translational science. Elucidating these methods could increase the ‘translational efficiency’ in studying the broader population health, the mechanisms, and impacts of one of the most pressing public health challenges – firearm injury – and identify opportunities for health interventions that mitigate adverse health outcomes. The central objectives of this proposal are 1) to develop methods to identify and link family and household members at the family and household level and 2) examine change in clinical care utilization, mental health, chronic conditions, and firearm injury among non- household family, household family members, and non-family household members of firearm injury survivors following the injury. To achieve this methodologic goal, we will leverage a unique longitudinal dataset of individually-linked data and residential addresses from clinical systems, public health, Medicaid, and police records in Indianapolis (Marion County), Indiana from 2000 – 2022. The proposed study is focused on nonfatal firearm injury, as an extension of Dr. Lauren Magee’s KL2 project and research portfolio; however, the methodological approaches can be applied to a variety of vulnerable populations to identify opportunities for intervention across a variety of health outcomes. Findings will demonstrate the feasibility of linking family and household members at the family and household level using administrative data and will significantly extend our understanding of the racial and health inequities associated with exposure to firearm injury beyond the direct survivor to inform clinical and community interventions to prevent future morbidity and mortality. This proposal will also address a significant roadblock for clinical and translational science and continue Dr. Magee’s development as a scholar in translational science.", "keywords": [ "Accident and Emergency department", "Acute", "Address", "Adoption", "Adult", "Affect", "Attention", "Birth", "Birth Certificates", "COVID-19 pandemic", "Caregivers", "Cessation of life", "Child", "Chronic", "Chronic stress", "Clinical", "Clinical Sciences", "Communities", "County", "Data", "Development", "Diagnosis", "Disease", "Event", "Exposure to", "Family", "Family member", "Firearms", "Future", "Goals", "Gun injury", "Health", "Healthcare", "Household", "Household and Family", "Indiana", "Individual", "Injury", "Inpatients", "Intervention", "Link", "Marriage", "Medicaid", "Mental Health", "Methodology", "Methods", "Morbidity - disease rate", "Names", "Nature", "Outcome", "Outpatients", "Parents", "Persons", "Police", "Population", "Public Health", "Qualitative Research", "Records", "Research", "Single Parent", "Social support", "Survivors", "System", "Time", "Translational Research", "Trauma", "Vulnerable Populations", "Woman", "Work", "Youth", "acute stress", "clinical care", "community intervention", "data repository", "experience", "health inequalities", "health record", "high risk", "indexing", "innovation", "insurance plan", "longitudinal dataset", "member", "mortality", "population health", "prevent", "racial disparity" ], "approved": true } }, { "type": "Grant", "id": "14704", "attributes": { "award_id": "1K23EY035741-01", "title": "Artificial Intelligence Analysis of Myopic Vitreoretinal Pathology", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Eye Institute (NEI)" ], "program_reference_codes": [], "program_officials": [ { "id": 31398, "first_name": "EDWIN C", "last_name": "Clayton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-03-01", "end_date": "2029-02-28", "award_amount": 274148, "principal_investigator": { "id": 31399, "first_name": "Cassie Ann", "last_name": "Ludwig", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "PROJECT SUMMARY: This application seeks a career development award for an academic vitreoretinal surgeon with an interest in high myopia, a condition which predisposes patients to potentially blinding complications including retinal tears (RTs) and rhegmatogenous retinal detachments (RRDs). This proposal is a 5-year curriculum and research plan to transition Dr. Cassie Ludwig to independence. The candidate is an accomplished early career physician-scientist who will undergo all training and execute the research noted herein during this period. Myopia affects one third of the world's population today and has been predicted to affect 50% of the world's population by 2050.1,2 Worse, this prediction is likely an underestimate as myopigenic behaviors have been further compounded by the COVID pandemic and digital remote learning.3–8 This increasing prevalence has significant consequences as each diopter of myopia increases the risk of retinal tears and detachments, myopic macular degeneration, choroidal neovascularization, myopic traction maculopathy, strabismus, glaucoma, and cataracts. Slowing myopia progression even minimally can help prevent blindness. Using combined data from five large population-based studies, Bullimore et al. found that slowing myopia by one diopter should reduce the likelihood of a patient developing an RRD by 30%.2 Electronic health records (EHRs) and ophthalmic imaging databases contain enormous quantities of systemic and ocular data generated by clinical practice which can be used to better understand the relationship between systemic and ophthalmic risk factors, myopia and RTs and RRDs. EHR and imaging data can be fused into predictive models that employ machine learning to risk-stratify patients. In this proposal, Dr. Ludwig aims to achieve the following: 1. Develop and validate a structured EHR deep learning framework to predict RT and RRD risk in myopes and non-myopes 2. Develop and validate an unstructured EHR transformer-based deep learning model to predict RT and RRD risk in myopes and non-myopes, and 3. Develop and validate an ultra-widefield photography convolutional neural network (CNN)-based deep learning model to predict RT and RRD risk in myopes and non-myopes. The central hypothesis is that modeling of attributes from EHR data and images can predict risk of RTs and RRDs. The principal investigator, Cassie A. Ludwig, MD, MS, will perform this research as part of a larger effort to obtain additional training and mentorship in biomedical informatics, artificial intelligence, biodesign, and myopia. Dr. Ludwig’s career development plan includes a PhD program with didactic coursework, conferences, workshops, and frequent communication and interaction with a network of mentors with an impressive abundance of their own NIH funding and prior mentorship experiences. This experience will guide Dr. Ludwig into a career as an independent clinician-scientist with expertise in artificial intelligence and a focus on myopia and its sequelae.", "keywords": [ "Affect", "Algorithms", "Artificial Intelligence", "Behavior", "Bioinformatics", "Blindness", "COVID-19 pandemic", "Cataract", "Choroidal Neovascularization", "Clinical", "Code", "Color", "Communication", "Cornea", "Data", "Databases", "Development Plans", "Diabetic Retinopathy", "Diagnosis", "Disease", "Distance Learning", "Doctor of Philosophy", "Educational Curriculum", "Educational workshop", "Electronic Health Record", "Faculty", "Funding", "Fundus photography", "Glaucoma", "Goals", "Image", "K-Series Research Career Programs", "Knowledge", "Length", "Light", "Link", "Logistic Regressions", "Machine Learning", "Macular degeneration", "Mentors", "Mentorship", "Methodology", "Methods", "Modeling", "Myopia", "Natural Language Processing", "Optical Coherence Tomography", "Outcome", "Pathologic", "Pathology", "Patients", "Photography", "Physicians", "Population", "Population Study", "Prevalence", "Prevention", "Principal Investigator", "Prophylactic treatment", "Provider", "Radiology Specialty", "Reporting", "Research", "Retina", "Retinal Detachment", "Retinal Diseases", "Retinal Perforations", "Risk", "Risk Factors", "Scientist", "Sensitivity and Specificity", "Statistical Methods", "Strabismus", "Structure", "Surgeon", "Symptoms", "Testing", "Text", "Thick", "Traction", "Training", "United States National Institutes of Health", "Validation", "Vision", "Visual", "artificial intelligence algorithm", "biomedical informatics", "career", "career development", "clinical encounter", "clinical practice", "cohort", "convolutional neural network", "deep learning", "deep learning model", "deep neural network", "demographics", "digital", "disorder of macula of retina", "electronic structure", "experience", "high risk", "improved", "interest", "machine learning model", "ophthalmic examination", "patient stratification", "predictive modeling", "prevent", "programs", "prophylactic", "repaired", "retinal imaging", "risk prediction", "risk stratification", "standard of care", "symposium", "training opportunity", "vitreous floater" ], "approved": true } }, { "type": "Grant", "id": "14719", "attributes": { "award_id": "1R01HL173128-01", "title": "Semiparametric Regression Analysis of Interval-Censored Data in Current Cohort Studies", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 31410, "first_name": "Michael", "last_name": "Wolz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-03-10", "end_date": "2028-02-29", "award_amount": 396020, "principal_investigator": { "id": 23432, "first_name": "Donglin", "last_name": "Zeng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 770, "ror": "", "name": "UNIVERSITY OF MICHIGAN AT ANN ARBOR", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "In epidemiological cohort studies, the onset of an asymptomatic disease (e.g., diabetes, hypertension, chronic obstructive pulmonary disease, HIV infection, SARS-CoV-2 infection, cancer, or dementia) cannot be observed directly but rather is known to occur sometime between two consecutive clinical examinations. The two examina- tions bookend a time interval, such that the event time is “interval-censored”. It is highly challenging to analyze interval-censored data because none of the event times is exactly known; therefore, investigators have resorted to statistical methods that are unreliable or even invalid. The broad, long-term objectives of this research project are to develop semiparametric regression models, with associated inference procedures and numerical algo- rithms, for analyzing interval-censored data from current epidemiological investigations. The specific aims of the project are: (1) to explore semiparametric regression models for assessing the impact of an interval-censored event (e.g., onset of diabetes) on future outcomes (e.g., stroke, heart attack, methylation level); (2) to build a system of proportional intensity models with random effects for analyzing interval-censored multi-state processes that characterize disease progression over time; (3) to provide graphical and numerical techniques for checking the adequacy of semiparametric regression models with interval-censored data; and (4) to relax the proportional hazards assumption by allowing time-varying regression coefficients. All of these aims are motivated by the unmet methodological needs in the cohort studies that the investigators are currently conducting and address the most timely and important issues in human population health research. The estimation of model parame- ters is based on nonparametric likelihood (with an arbitrary event-time distribution) and other sound statistical principles. The large-sample properties of the estimators will be established rigorously through innovative use of modern empirical process theory, semiparametric efficiency theory, and other advanced mathematical argu- ments. Computationally efficient and stable algorithms will be created to implement the inference procedures. The operating characteristics of the numerical algorithms and inference procedures will be evaluated extensively through simulation studies that mimic real data. The proposed methods will be applied to the Atherosclerosis Risk in Communities Study and the SubPopulations and InteRmediate Outcome Measures In COPD Study, both of which are being carried out at the University of North Carolina at Chapel Hill. These studies exemplify the broad challenges and opportunities arising from modern epidemiological research. The results will be published in both statistical and medical journals. Efficient, reliable, user-friendly, open-access, and well-documented R packages will be produced and disseminated to the broad scientific community. This research will create new paradigms in survival analysis, advance population health research in the United States and elsewhere, and accelerate the search for effective strategies to prevent and treat many diseases of critical importance to public health, including cardiovascular disease, lung disease, diabetes, cancer, HIV/AIDS, and dementia.", "keywords": [ "Acceleration", "Address", "Algorithms", "Atherosclerosis Risk in Communities", "COVID-19", "Cardiovascular Diseases", "Cessation of life", "Characteristics", "Chronic Obstructive Pulmonary Disease", "Cohort Studies", "Communities", "Computer software", "Cox Models", "Cox Proportional Hazards Models", "Data", "Data Set", "Dementia", "Diabetes Mellitus", "Disease", "Disease Progression", "Documentation", "Epidemiology", "Equation", "Event", "Future", "Gills", "Goals", "HIV Infections", "HIV/AIDS", "Heart Diseases", "Human", "Hypertension", "Infection", "Intervention", "Journals", "Life", "Likelihood Functions", "Link", "Lung Diseases", "Malignant Neoplasms", "Mathematics", "Maximum Likelihood Estimate", "Medical", "Methodology", "Methods", "Methylation", "Modeling", "Modernization", "Monte Carlo Method", "Myocardial Infarction", "Normalcy", "North Carolina", "Outcome", "Outcome Measure", "Participant", "Periodicals", "Prevention strategy", "Probability", "Procedures", "Process", "Property", "Proportional Hazards Models", "Public Health", "Publishing", "Regression Analysis", "Relaxation", "Research", "Research Personnel", "Research Project Grants", "Resort", "SARS-CoV-2 infection", "Sampling", "Statistical Methods", "Stochastic Processes", "Stroke", "Survival Analysis", "System", "Techniques", "Time", "United States", "Universities", "Work", "absorption", "clinical examination", "effective intervention", "epidemiologic data", "epidemiology study", "expectation", "experience", "follow-up", "hazard", "high dimensionality", "innovation", "interest", "novel", "population health", "prevent", "primary outcome", "semiparametric", "simulation", "sound", "theories", "time interval", "tool", "user-friendly" ], "approved": true } } ], "meta": { "pagination": { "page": 1383, "pages": 1424, "count": 14236 } } }