Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=title
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=title", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=title", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=title", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=title" }, "data": [ { "type": "Grant", "id": "7858", "attributes": { "award_id": "1R01HL157985-01A1", "title": "VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19", "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": 6344, "first_name": "Lora A.", "last_name": "Reineck", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-15", "end_date": "2026-01-31", "award_amount": 700109, "principal_investigator": { "id": 23694, "first_name": "Atul", "last_name": "Malhotra", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 760, "ror": "https://ror.org/0168r3w48", "name": "University of California, San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 23695, "first_name": "Shamim", "last_name": "Nemati", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 760, "ror": "https://ror.org/0168r3w48", "name": "University of California, San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has led to massive challenges for health care systems and for global economics. The surge in cases which occurred abruptly strained the existing resources to care for the volume of patients, leading to a shortage of supply in many medications, personnel and equipment. The mechanical ventilator became a particular problem as one newly published study reported that 18 out of 24 patients with COVID-19 in the study (75%) required mechanical ventilation. During the early months of the pandemic many providers decided to intubate early on the assumption that patients would eventually need mechanical ventilation so as to avoid ‘crash intubation’ and potential contamination. A recent observational study of intensive care unit patients with COVID-19 suffering from acute hypoxemic respiratory failure revealed that early invasive mechanical ventilation was associated with an increased risk of day-60 mortality. One central problem in this context was caregivers’ inability to predict which patients may need mechanical ventilation since existing methods using clinical parameters are often subjective and inconsistent across different institutions. We have thus applied machine learning algorithms to commonly available data in electronic health records (EHR) to develop and validate a predictive model for 24-hours ahead prediction of respiratory failure. This novel predictive model has demonstrated AUCs in the range of 0.90-0.94 in our internal and external COVID-19 datasets. That is, we have a robust ability now to predict which patients may need mechanical ventilation and which will not. We are now planning to deploy clinically and to improve iteratively on our model by adding other data streams such as imaging to not only improve our predictive ability but also to make the predictions more ‘actionable’, so that clinicians can pursue timely interventions rather than just being told a prognosis. We are further addressing the many barriers to implementation by addressing ‘clinician buy-in’ which involves making the underlying reasoning of our algorithms more transparent, making the predictions seamlessly integrated into clinical workflow, and finding actionable parameters that will allow both predictions and therapeutic interventions. Such an algorithm will enhance the ability of clinicians to estimate the risk for respiratory failure, and ideally, to anticipate and respond to patient needs in a timely fashion. Moreover, given a long enough prediction horizon (48-72 hours) such systems can facilitate triage and optimization of related resources (ventilators and personnel) within a given hospital and across healthcare systems. Finally, while the COVID-19 pandemic highlighted the need for optimizing the timing of mechanical ventilation, the techniques developed under this proposal are broadly applicable to other causes of respiratory failure and to other types of organ support technologies.", "keywords": [ "Address", "Adoption", "Algorithms", "Area", "Awareness", "COVID-19", "COVID-19 pandemic", "COVID-19 patient", "Caregivers", "Caring", "Clinical", "Clinical Data", "Computers", "Confidence Intervals", "Critical Care", "Critical Illness", "Data", "Data Set", "Deterioration", "Economics", "Electronic Health Record", "Equipment", "Health Personnel", "Healthcare Systems", "Hospitals", "Hour", "Human Resources", "Image", "Impairment", "Institution", "Intensive Care Units", "Intervention", "Intratracheal Intubation", "Intubation", "Judgment", "Laboratories", "Measures", "Mechanical Ventilators", "Mechanical ventilation", "Methodology", "Methods", "Modeling", "Morbidity - disease rate", "Observational Study", "Organ", "Patient Care", "Patients", "Pattern", "Pharmaceutical Preparations", "Prognosis", "Provider", "Publishing", "Reporting", "Research", "Research Personnel", "Resource Allocation", "Resources", "Respiratory Disease", "Respiratory Failure", "Risk", "Risk Estimate", "Running", "System", "Systems Integration", "Techniques", "Technology", "Testing", "Therapeutic Intervention", "Thoracic Radiography", "Time", "Triage", "Ventilator", "Work", "acute hypoxemic respiratory failure", "base", "clinical care", "clinical decision support", "clinical predictors", "data integration", "data streams", "deep learning", "deep learning algorithm", "deep learning model", "design", "health care delivery", "implementation barriers", "improved", "innovation", "machine learning algorithm", "mortality", "multidisciplinary", "multimodal data", "multimodality", "novel", "pandemic disease", "portability", "prediction algorithm", "predictive modeling", "prospective test", "risk prediction", "serial imaging", "skills", "support tools", "supportive environment" ], "approved": true } }, { "type": "Grant", "id": "4992", "attributes": { "award_id": "1R43AI167263-01A1", "title": "Very rapid, low cost multiplexed test for SARS, Influenza A and Influenza B Resubmission", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 17921, "first_name": "DIPANWITA", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-15", "end_date": "2023-03-31", "award_amount": 285597, "principal_investigator": { "id": 17922, "first_name": "GREGORY W", "last_name": "FARIS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 419, "ror": "", "name": "NUMENTUS TECHNOLOGIES INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The objective of this project is to develop a point-of-contact molecular diagnostic technology (test) for multi- plexed detection of a coronavirus (SARS-CoV-2) and influenza virus. While a point-of-care diagnostic might be performed in a period of 10-20 minutes in a clinical setting, we envision a point-of-contact test being performed about 10 times faster and before someone passes through a door, checkpoint, airport gate, or border. We have identified a strategy for performing PCR that uses a new format to speed thermal cycling while achieving ap- proximately 1,000,000-fold sample partitioning to accelerate sample preparation, without micro-patterning or microfluidics. As described below, we have already established critical strategies for the project including (1) rapid, uniform cycling using optical heating; (2) large scale sample partitioning; and (3) a one-step lysis/RT- PCR assay for enveloped viruses, including SARS-CoV-2 (the virus responsible for COVID-19). This project will provide the basis for a multiplexed point-of-contact diagnostic that can be performed in as little as 2 minutes and at a cost as low as $2/test for Bill of Material (BOM) consumables. When available, this diagnostic platform could be used routinely for detecting hidden spreaders of disease at, e.g., airports, en- trances to hospitals, or at long-term care facilities. Availability of the diagnostic would be transformative. Be- cause of its speed and low cost, the platform could provide the first point-of-contact molecular diagnostic for diseases such as COVID-19, which could detect asymptomatic spreaders, e.g., as they embark or disembark at airports. Rapid deployment of the test at borders or entry points could prevent disease from spreading be- yond an initial outbreak. Similarly, the test could be used to screen employees when they arrive to work at health care facilities to protect patients and essential workers. Finally, the test could be used for routine screening at large facilities such as factories, food processing or distribution facilities, and large government buildings, reducing the economic impact of a addressing a viral pandemic such as COVID-19.", "keywords": [ "2019-nCoV", "Address", "Antibodies", "Base Sequence", "Biological Assay", "Blood", "Buffers", "Businesses", "Butterflies", "COVID-19", "COVID-19 assay", "COVID-19 pandemic", "Clinical", "Collection", "Computer software", "Coronavirus", "Cytolysis", "Detection", "Development", "Diagnostic", "Disease", "Disease Outbreaks", "Economics", "Employee", "Food Processing", "Freeze Drying", "Future", "Government", "Health care facility", "Heating", "Hospitals", "Immune response", "Immunoassay", "Infection", "Influenza", "Influenza A virus", "Influenza B Virus", "International", "Investigation", "Laboratories", "Life", "Long-Term Care", "Mass Screening", "Measurement", "Methods", "Microfluidics", "Middle East Respiratory Syndrome", "Molecular Diagnostic Testing", "Monitor", "Morbidity - disease rate", "Optics", "Patients", "Pattern", "Pensions", "Persons", "Phase", "Population", "Preparation", "Prisons", "Procedures", "Production", "RNA", "Reaction", "Reagent", "Reporting", "Respiratory Disease", "Reverse Transcriptase Polymerase Chain Reaction", "Safety", "Saliva", "Sampling", "Sensitivity and Specificity", "Severe Acute Respiratory Syndrome", "Societies", "Speed", "Sputum", "Swab", "System", "Testing", "Time", "Transportation", "Tube", "Viral", "Virus", "Virus Diseases", "Work", "Workplace", "antigen test", "base", "biosafety level 3 facility", "brass", "coronavirus disease", "cost", "diagnostic platform", "diagnostic screening", "diagnostic technologies", "disease diagnostic", "disease transmission", "economic impact", "gamma irradiation", "improved", "influenzavirus", "instrument", "instrumentation", "lateral flow assay", "melting", "molecular diagnostics", "mortality", "multiplex assay", "multiplex detection", "nasopharyngeal swab", "novel", "novel diagnostics", "pandemic disease", "point of care", "point-of-care diagnostics", "prevent", "respiratory", "routine screening", "screening", "seal", "synthetic construct", "synthetic nucleic acid", "transmission process", "viral detection", "virtual", "virus envelope" ], "approved": true } }, { "type": "Grant", "id": "9321", "attributes": { "award_id": "1U18FD007244-01", "title": "Vet-LIRN antimicrobial resistance surveillance through collection of AMR data and whole genome sequencing of isolates from Canadian provinces", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24456, "first_name": "Olgica", "last_name": "Ceric", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-20", "end_date": "2025-08-31", "award_amount": 62823, "principal_investigator": { "id": 25069, "first_name": "Durda", "last_name": "Slavic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true }, "abstract": "As a part of the FDA Veterinary Laboratory Information Response Network (Vet-LIRN) initiative ‘to support enhanced human and animal food safety’ Animal Health Laboratory (AHL) acquired MiSeq Illumina and was tasked to established a Canadian veterinary diagnostics laboratory unit (5 source labs and 1 WGS regional lab) to support and expand Vet-LIRN's Antimicrobial Resistance-Whole Genome Sequencing (AMR-WGS) program. This unit was established in 2018 and the program ran throughout 2019 with bacterial isolates (604), metadata and antimicrobial susceptibility results collection and whole genome sequencing (WGS) of selected isolates (270). The isolate collection slowed down in the second quarter of 2020 because of COVID-19, and also one laboratory dropped out of the program due to a staffing shortage. As a result, our objectives for this multiyear project are: 1. To recruit another source laboratory for isolate, metadata and antimicrobial susceptibility results collection for 2021. 2. To continue established collaboration with other source laboratories by providing them with supplies and covering quarterly shipping expenses for isolates and data collection. 3. To continue with isolates collection at AHL. 4. To increase surge capacity and capability of AHL in order to aid Vet-LIRN AMR surveillance efforts and clinical disease investigations using harmonized methodology by: a. Using Illumina MiSeq to do WGS of bacterial isolates following protocols provided by Vet-LIRN. b. Participating in proficiency testing provided by GenomeTrakr. c. Submitting sequence information to NCBI to make them publicly available.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9322", "attributes": { "award_id": "3U18FD007244-02S1", "title": "Vet-LIRN antimicrobial resistance surveillance through collection of AMR data and whole genome sequencing of isolates from Canadian provinces", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24456, "first_name": "Olgica", "last_name": "Ceric", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-20", "end_date": "2025-08-31", "award_amount": 17813, "principal_investigator": { "id": 25069, "first_name": "Durda", "last_name": "Slavic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true }, "abstract": "As a part of the FDA Veterinary Laboratory Information Response Network (Vet-LIRN) initiative ‘to support enhanced human and animal food safety’ Animal Health Laboratory (AHL) acquired MiSeq Illumina and was tasked to established a Canadian veterinary diagnostics laboratory unit (5 source labs and 1 WGS regional lab) to support and expand Vet-LIRN's Antimicrobial Resistance-Whole Genome Sequencing (AMR-WGS) program. This unit was established in 2018 and the program ran throughout 2019 with bacterial isolates (604), metadata and antimicrobial susceptibility results collection and whole genome sequencing (WGS) of selected isolates (270). The isolate collection slowed down in the second quarter of 2020 because of COVID-19, and also one laboratory dropped out of the program due to a staffing shortage. As a result, our objectives for this multiyear project are: 1. To recruit another source laboratory for isolate, metadata and antimicrobial susceptibility results collection for 2021. 2. To continue established collaboration with other source laboratories by providing them with supplies and covering quarterly shipping expenses for isolates and data collection. 3. To continue with isolates collection at AHL. 4. To increase surge capacity and capability of AHL in order to aid Vet-LIRN AMR surveillance efforts and clinical disease investigations using harmonized methodology by: a. Using Illumina MiSeq to do WGS of bacterial isolates following protocols provided by Vet-LIRN. b. Participating in proficiency testing provided by GenomeTrakr. c. Submitting sequence information to NCBI to make them publicly available.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9323", "attributes": { "award_id": "5U18FD007244-02", "title": "Vet-LIRN antimicrobial resistance surveillance through collection of AMR data and whole genome sequencing of isolates from Canadian provinces", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24456, "first_name": "Olgica", "last_name": "Ceric", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-20", "end_date": "2025-08-31", "award_amount": 72317, "principal_investigator": { "id": 25069, "first_name": "Durda", "last_name": "Slavic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1822, "ror": "https://ror.org/01r7awg59", "name": "University of Guelph", "address": "", "city": "", "state": "ON", "zip": "", "country": "CANADA", "approved": true }, "abstract": "As a part of the FDA Veterinary Laboratory Information Response Network (Vet-LIRN) initiative ‘to support enhanced human and animal food safety’ Animal Health Laboratory (AHL) acquired MiSeq Illumina and was tasked to established a Canadian veterinary diagnostics laboratory unit (5 source labs and 1 WGS regional lab) to support and expand Vet-LIRN's Antimicrobial Resistance-Whole Genome Sequencing (AMR-WGS) program. This unit was established in 2018 and the program ran throughout 2019 with bacterial isolates (604), metadata and antimicrobial susceptibility results collection and whole genome sequencing (WGS) of selected isolates (270). The isolate collection slowed down in the second quarter of 2020 because of COVID-19, and also one laboratory dropped out of the program due to a staffing shortage. As a result, our objectives for this multiyear project are: 1. To recruit another source laboratory for isolate, metadata and antimicrobial susceptibility results collection for 2021. 2. To continue established collaboration with other source laboratories by providing them with supplies and covering quarterly shipping expenses for isolates and data collection. 3. To continue with isolates collection at AHL. 4. To increase surge capacity and capability of AHL in order to aid Vet-LIRN AMR surveillance efforts and clinical disease investigations using harmonized methodology by: a. Using Illumina MiSeq to do WGS of bacterial isolates following protocols provided by Vet-LIRN. b. Participating in proficiency testing provided by GenomeTrakr. c. Submitting sequence information to NCBI to make them publicly available.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11747", "attributes": { "award_id": "1U18FD008009-01", "title": "Vet-LIRN Network Capacity Building for SARS-CoV-2: equipment for analysis of NGS library and data", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 26272, "first_name": "Megan", "last_name": "Miller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-01", "end_date": "2024-06-30", "award_amount": 52822, "principal_investigator": { "id": 24756, "first_name": "Leyi", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1040, "ror": "", "name": "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1040, "ror": "", "name": "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The University of Illinois (UI) Veterinary Diagnostic Laboratory (VDL) is a full service, AAVLD accredited, all species, reference laboratory. UI VDL is a member of the VET-LIRN. As of Feb 17, 2023, UI VDL has tested 3,287 samples for SARS-CoV-2 and 350 samples of zoo animals in 13 different zoos were tested positive. Next- generation sequencing (NGS) is very useful in case investigation of SARS-CoV-2 in the veterinary diagnostic laboratory and could be affected by many factors including selection of sequencing methods, sequencing library quality, amount of viral genome, sample quality, sample pooling, and bioinformatics analysis. New equipment for analyzing both NGS library and big data will significantly increase our SARS-CoV-2 testing capacity and service quality for routine case diagnosis and surveillance coordinated by Vet-LIRN, thus enhancing Vet-LIRN network laboratory for SARS-CoV-2 testing.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "6065", "attributes": { "award_id": "3R25GM137169-02S1", "title": "VetaHumanz Need Vaccines Too!", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 20674, "first_name": "LAWRENCE A.", "last_name": "BECK", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2025-07-31", "award_amount": 54000, "principal_investigator": { "id": 20675, "first_name": "Sandra F", "last_name": "San Miguel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1139, "ror": "", "name": "PURDUE UNIVERSITY", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1139, "ror": "", "name": "PURDUE UNIVERSITY", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "T he parent grant, See Us-Be Us, responds to critical gaps in K-1 2 ST EM education, by increasing (1 ) v isibility of veterinary role models diverse in race and ethnicity, (2) access to veterinary STEM educational materials, and (3) access to experiential learning opportunities. Since the start of funding on September 1, 2020, the veterinary STEM ecosystem proposed in the parent grant has expanded beyond academia by transforming into a veterinary superhero league, The League of VetaHumanz. VetaHumanz are human beings (veterinarians) with superpowers who protect animal and public health. As a nation, we face the challenge of increasing vaccine acceptance by underserved populations who have been disproportionately impacted by SARS-CoV-2, lack access to healthcare, and have a longstanding mistrust of medical establishments resulting from a history abuse and exploitation. This administrative supplement, VetaHumanz Need Vaccines Too!, aims to promote SARS-CoV-2 vaccination uptake in underserved communities by educating children and their families on the importance of vaccination, empowering children to becoming advocates for vaccination, and inspiring children to explore veterinary careers that impact public health. Our focus on early elementary school children is innovative because we have an opportunity to educate underserved children who have not yet developed a long-term mistrust of our medical system. In the short term, children can positively impact the health of their communities by advocating for vaccination. In the long term, children can change the narrative by aspiring to become veterinary scientists that better represent the US population and are worthy of public trust. We hypothesize that after becoming informed about vaccines and the importance of SARS-CoV-2 vaccination, children will actively promote vaccination in their communities and aspire to become veterinarians. This supplement has three specific aims. T he focus of Aim 1 is to dev elop a Vaccine SuperPower Pack. Vaccine SuperPower Packs will contain educational materials for children about the importance of vaccines. Packs will also encourage pursuit of veterinary careers and communication of scientific knowledge by featuring the superhero veterinary scientist who hosts the VetaHumanz Live! podcast. Aim 2 is focused on promoting SARS- CoV-2 vaccination by featuring veterinarians who have been involved in the response to SARS-CoV-2 through the VetaHumanz Live! podcast which will be broadcast through our website and community events. Aim 3 promotes SARS-CoV-2 vaccination through a League of VetaHumanz social media campaign. Our League of VetaHumanz, a far-reaching, inclusive veterinary STEM ecosystem consisting of veterinary professionals, veterinary students, and community entities serving disadvantaged youth, will provide the frameworkfor distribution of educational resources and messaging. VetaHumanz will educate children as well as model being vaccinated for SARS-CoV-2 on social media campaigns, because even superheroes need vaccines.", "keywords": [ "2019-nCoV", "Academia", "Active Learning", "Administrative Supplement", "Advocate", "Animals", "COVID-19 vaccination", "Child", "Communication", "Communities", "Disadvantaged", "Ecosystem", "Education", "Educational Materials", "Ethnic Origin", "Event", "Face", "Family", "Funding", "Health", "Human", "Knowledge", "Media Campaign", "Medical", "Modeling", "Population", "Public Health", "Race", "Recording of previous events", "Scientist", "Students", "System", "Underserved Population", "Vaccinated", "Vaccination", "Vaccines", "Veterinarians", "Youth", "career", "education resources", "elementary school", "empowered", "health care availability", "innovation", "parent grant", "podcast", "public trust", "response", "role model", "social media", "underserved community", "vaccine acceptance", "web site" ], "approved": true } }, { "type": "Grant", "id": "11005", "attributes": { "award_id": "5P50MD017349-02", "title": "VIDA: Virtual Diabetes Group Visits Across Health Systems", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Minority Health and Health Disparities (NIMHD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2021-09-24", "end_date": "2026-06-30", "award_amount": 622687, "principal_investigator": { "id": 24458, "first_name": "Arshiya Ahmed", "last_name": "Baig", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Of the 30 million adults with DM in the United States, 97% have at least one comorbid condition (e.g. hypertension, heart disease, kidney disease). DM and DM-related complications disproportionately affect people of color. The prevalence of DM is higher among Hispanics (12.5%) and African-Americans (11.7%) compared to non-Hispanic whites (7.5%); Hispanics and African-Americans have higher rates of diabetes-related complications, including amputations and CKD. Group visits (GVs) can provide patients with comprehensive care for their multimorbid chronic condition. Diabetes GVs—shared appointments where patients receive self- management education in a group setting and an individual medical visit—can improve glycemic control, decrease healthcare utilization, and provide social support and co-learning among peers. While virtual appointments have become a routine part of clinical care during the COVID-19 pandemic, group visits via virtual platforms remain uncommon and understudied. Before the model can be widely adopted, important questions about the effectiveness and implementation of the virtual diabetes GV model need to be addressed. We propose to build on an established program of in-person diabetes GVs and a virtual diabetes GV pilot by the University of Chicago and MidWest Clinician’s Network. We aim to implement the virtual GV model (VIDA: Virtual Diabetes Group Visits Across Health Systems) in two distinct health systems in the Chicago region: ACCESS and Advocate Aurora Health (AAH). ACCESS is one of the largest federally qualified health centers (FQHCs) in the U.S. with 35 sites across the Chicago metropolitan area, providing care for 175,000 medically underserved and low-income patients each year, including over 25,000 patients with diabetes. Advocate Aurora Health (AAH) is a large, diverse, integrated private not-for profit health system with more than 129 primary care clinics in Illinois serving over 117,000 patients with diabetes. The ability to train, implement and evaluate virtual group visits across two distinct health systems provides a unique opportunity to learn about adaptation and the barriers and facilitators for program implementation. This study will use a type I hybrid effectiveness-implementation design via a pragmatic cluster randomized trial to assess changes in clinical outcomes among adults with T2DM in virtual diabetes GVs versus usual care. We will first adapt and implement VIDA at one ACCESS FQHC center and one AAH primary care clinic using the Form and Function domains of the Complex Health Intervention Framework. We will assess integration of VIDA into clinical workflow and determine the type of and amount of training and technical support needed to assist staff in integrating virtual diabetes GV into the clinical setting. We will then conduct a pragmatic cluster randomized trial of virtual GVs across 9 intervention sites (180 adult patients with T2DM with A1C >9%) and 9 control sites (360 matched patients) and assess change in A1C from baseline to 12-months and change in other clinical outcomes including systolic blood pressure and body mass index. We will assess adoption, implementation, and maintenance of virtual GVs across systems using RE-AIM framework.", "keywords": [ "Chicago", "Chronic", "Diabetes Mellitus", "Health system", "Visit", "virtual" ], "approved": true } }, { "type": "Grant", "id": "8683", "attributes": { "award_id": "1P50MD017349-01", "title": "VIDA: Virtual Diabetes Group Visits Across Health Systems", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Minority Health and Health Disparities (NIMHD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2021-09-24", "end_date": "2026-06-30", "award_amount": 674542, "principal_investigator": { "id": 24458, "first_name": "Arshiya Ahmed", "last_name": "Baig", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Of the 30 million adults with DM in the United States, 97% have at least one comorbid condition (e.g. hypertension, heart disease, kidney disease). DM and DM-related complications disproportionately affect people of color. The prevalence of DM is higher among Hispanics (12.5%) and African-Americans (11.7%) compared to non-Hispanic whites (7.5%); Hispanics and African-Americans have higher rates of diabetes-related complications, including amputations and CKD. Group visits (GVs) can provide patients with comprehensive care for their multimorbid chronic condition. Diabetes GVs—shared appointments where patients receive self- management education in a group setting and an individual medical visit—can improve glycemic control, decrease healthcare utilization, and provide social support and co-learning among peers. While virtual appointments have become a routine part of clinical care during the COVID-19 pandemic, group visits via virtual platforms remain uncommon and understudied. Before the model can be widely adopted, important questions about the effectiveness and implementation of the virtual diabetes GV model need to be addressed. We propose to build on an established program of in-person diabetes GVs and a virtual diabetes GV pilot by the University of Chicago and MidWest Clinician’s Network. We aim to implement the virtual GV model (VIDA: Virtual Diabetes Group Visits Across Health Systems) in two distinct health systems in the Chicago region: ACCESS and Advocate Aurora Health (AAH). ACCESS is one of the largest federally qualified health centers (FQHCs) in the U.S. with 35 sites across the Chicago metropolitan area, providing care for 175,000 medically underserved and low-income patients each year, including over 25,000 patients with diabetes. Advocate Aurora Health (AAH) is a large, diverse, integrated private not-for profit health system with more than 129 primary care clinics in Illinois serving over 117,000 patients with diabetes. The ability to train, implement and evaluate virtual group visits across two distinct health systems provides a unique opportunity to learn about adaptation and the barriers and facilitators for program implementation. This study will use a type I hybrid effectiveness-implementation design via a pragmatic cluster randomized trial to assess changes in clinical outcomes among adults with T2DM in virtual diabetes GVs versus usual care. We will first adapt and implement VIDA at one ACCESS FQHC center and one AAH primary care clinic using the Form and Function domains of the Complex Health Intervention Framework. We will assess integration of VIDA into clinical workflow and determine the type of and amount of training and technical support needed to assist staff in integrating virtual diabetes GV into the clinical setting. We will then conduct a pragmatic cluster randomized trial of virtual GVs across 9 intervention sites (180 adult patients with T2DM with A1C >9%) and 9 control sites (360 matched patients) and assess change in A1C from baseline to 12-months and change in other clinical outcomes including systolic blood pressure and body mass index. We will assess adoption, implementation, and maintenance of virtual GVs across systems using RE-AIM framework.", "keywords": [ "Address", "Adopted", "Adoption", "Adult", "Advocate", "Affect", "African American", "Amputation", "Appointment", "Area", "Blood Glucose", "Blood Pressure", "Body mass index", "COVID-19", "COVID-19 pandemic", "Cardiovascular system", "Caring", "Chicago", "Chronic", "Chronic Kidney Failure", "Clinic", "Clinical", "Cluster randomized trial", "Color", "Communities", "Community Health", "Community Health Networks", "Complex", "Complications of Diabetes Mellitus", "Comprehensive Health Care", "Diabetes Mellitus", "Diet", "Education", "Effectiveness", "Enrollment", "Federally Qualified Health Center", "Glycosylated Hemoglobin", "Glycosylated hemoglobin A", "Health", "Health Personnel", "Health system", "Heart Diseases", "Hispanic Americans", "Hispanics", "Hospitals", "Hypertension", "Illinois", "Individual", "Intervention", "Interview", "Kidney Diseases", "Latino", "Learning", "Low income", "Maintenance", "Medical", "Mental Depression", "Midwestern United States", "Modeling", "Non-Insulin-Dependent Diabetes Mellitus", "Not Hispanic or Latino", "Outcome", "Patient Appointment", "Patient Care Team", "Patients", "Persons", "Pharmaceutical Preparations", "Prevalence", "Primary Health Care", "Privatization", "Reach Effectiveness Adoption Implementation and Maintenance", "Self Management", "Site", "Social support", "System", "Testing", "Training", "Translational Research", "Uninsured", "United States", "Universities", "Visit", "Work", "acceptability and feasibility", "clinical care", "clinical research site", "comorbidity", "diabetes management", "effectiveness implementation design", "effectiveness implementation study", "glycemic control", "health care service utilization", "implementation facilitators", "implementation outcomes", "improved", "medically underserved", "metropolitan", "multiple chronic conditions", "peer", "primary care setting", "primary outcome", "programs", "secondary outcome", "telehealth", "treatment as usual", "trend", "virtual", "virtual platform" ], "approved": true } }, { "type": "Grant", "id": "15253", "attributes": { "award_id": "1R21AR083058-01A1", "title": "Video Disease Activity Index: A novel video measure to monitor rheumatoid arthritis in telehealth", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 26899, "first_name": "Su-Yau", "last_name": "Mao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-20", "end_date": "2026-06-30", "award_amount": 202312, "principal_investigator": { "id": 31841, "first_name": "ROBERT D", "last_name": "HOWE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Title Video Disease Activity Index: a video measure to monitor rheumatoid arthritis in telemedicine Project Summary/Abstract Rheumatoid arthritis (RA) is a degenerative autoimmune condition of the joints that is treated with medications that suppress the immune system. Medications must be regularly adjusted according to disease activity, based on clinical examination. Telemedicine has gained a key role in rheumatology during the COVID-19 pandemic, with rheumatologists amending treatment based on patient reports of disease activity and signs of swelling observed on video. However, numerous studies have proven that telemedicine may miss critical information that affects how disease activity is treated. The proposed project aims to improve the accuracy of assessing disease activity and functioning in RA during telemedicine visits by using laptops and smartphone standard cameras. This will also help reduce the costs associated with objective evaluation for RA. The overall goal of this exploratory and developmental R21 project is to assess technical feasibility and patient usability of camera- based remote assessment system. The first aim is to develop a web-based system that leverages computer vision to quantify joint range of motion and joint thickness as an indication of joint swelling in RA to determine disease activity. We also aim to modify and improve the current method of assessing functional impairment by incorporating isometric grip strength using an in-house squeezable ball. During this first phase, the vision-based system and the squeezable ball will be validated on young and older adults through comparison with gold- standard techniques (e.g., motion capture). The second aim is to evaluate a new scoring system called the Video Disease Activity Index (VDAI) in a cross-sectional feasibility investigation with RA patients (n = 50). The VDAI scoring system will be produced by quantifying joint range of motion and joint thickness to determine the number of swollen joints. This will provide a measure of disease activity that aligns with the clinically endorsed Clinical Disease Activity Index (CDAI), which will be measured by a rheumatologist in the clinic. Two tests of the VDAI will be conducted: one with a researcher present to evaluate the system's sensitivity and reliability against clinical examination (CDAI), and another where RA patients will use the web-based application alone, while still in the clinic, with a vision-based feedback algorithm to perform the required activities. As the VDAI and CDAI are on the same scale (0–24), the study will use joint-level power calculations to permit standard limits of agreement analysis between the two measures (e.g., Bland-Altman). The correlation between grip strength and functional questionnaire scores will also be evaluated using Pearson's tests. These analyses will provide a robust evaluation of the sensitivity and reliability of the VDAI system and its potential to improve the accuracy of assessing disease activity and functioning in RA during telemedicine visits. By leveraging ubiquitous cameras, this investigation has the potential to significantly increase the quality of disease activity and functioning in RA during telemedicine visits.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1405, "count": 14046 } } }