Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-program_officials
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N3C is a partnership among the NCATS-supported Clinical and Translational Science Awards (CTSA) Program hubs, the National Center for Data to Health (CD2H), and the NIGMS-supported Institutional Development Award Networks for Clinical and Translational Research (IDeA-CTR), with overall stewardship by NCATS. The N3C Data Enclave is a secure platform storing harmonized clinical data provided by more than 60 contributing members. The Enclave hosts over 670 million clinical observations on over 6.8 million persons, including over 2.2 million COVID cases, amounting to more than 7.8 billion rows of data. To protect privacy, this data consists only of limited data sets, de-identified data sets, and synthetic data sets; there is no personally identifiable information kept in the Enclave. The Enclave resides in the NCATS Secure Scientific Platforms Environment. The Environment is a specialized cloud-based data aggregation and analytics enclave that can integrate, manage, secure, and analyze any kind of scientific data, and provide secure, controlled access to internal and external collaborators. Within the Environment, multiple NIH ICs, Federal agencies, and Federal task forces integrate, manage, secure, and analyze all types of scientific data using dedicated platforms, and, equally importantly, make that data available in specific and controlled collaborations with each other and with external collaborators.", "keywords": [ "Advisory Committees", "Award", "COVID-19", "Clinical", "Clinical Data", "Clinical Research", "Clinical and Translational Science Awards", "Collaborations", "Data", "Data Aggregation", "Data Analytics", "Data Set", "Development", "Environment", "Health", "National Center for Advancing Translational Sciences", "National Institute of General Medical Sciences", "Personally Identifiable Information", "Persons", "Privacy", "Research", "Secure", "Translational Research", "United States National Institutes of Health", "cloud based", "cohort", "coronavirus disease", "data de-identification", "data enclave", "member", "programs" ], "approved": true } }, { "type": "Grant", "id": "10495", "attributes": { "award_id": "75N93022C00047-0-9999-1", "title": "REAL-TIME SURVEILLANCE OF VACCINE MISINFORMATION FROM SOCIAL MEDIA PLATFORMS", "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": [], "start_date": "2022-08-08", "end_date": "2023-08-07", "award_amount": 300000, "principal_investigator": { "id": 26502, "first_name": "JINGCHENG", "last_name": "DU", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1763, "ror": "", "name": "MELAX TECHNOLOGIES, INC.", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "To develop digital tools to identify and combat malicious digital bots that spread misinformation about infectious disease treatments and vaccines, including COVID-19 vaccines.", "keywords": [ "Basic Science", "COVID-19 vaccine", "Coronavirus", "Misinformation", "Severe Acute Respiratory Syndrome", "Time", "Vaccines", "combat", "digital", "infectious disease treatment", "social media", "tool" ], "approved": true } }, { "type": "Grant", "id": "10496", "attributes": { "award_id": "75N93022C00049-0-9999-1", "title": "DIGITAL TOOLS AGAINST MISINFORMATION ABOUT INFECTIOUS DISEASE TREATMENTS AND VACCINES", "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": [], "start_date": "2022-08-08", "end_date": "2023-08-07", "award_amount": 299993, "principal_investigator": { "id": 26503, "first_name": "KHAI", "last_name": "EDOUARD", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "To develop digital tools to identify and combat malicious digital bots that spread misinformation about infectious disease treatments and vaccines, including COVID-19 vaccines.", "keywords": [ "2019-nCoV", "Basic Science", "COVID-19 vaccine", "Coronavirus", "Misinformation", "Vaccines", "combat", "digital", "infectious disease treatment", "tool" ], "approved": true } }, { "type": "Grant", "id": "10497", "attributes": { "award_id": "75N93022C00050-0-9999-1", "title": "CAPTURING MEDICAL MISINFORMATION IN SOCIAL MEDIA USING AN ADVANCED AI SOLUTION SET", "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": [], "start_date": "2022-08-08", "end_date": "2023-08-07", "award_amount": 300000, "principal_investigator": { "id": 26504, "first_name": "MANOOCHEHR", "last_name": "GHIASSI", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "To develop digital tools to identify and combat malicious digital bots that spread misinformation about infectious disease treatments and vaccines, including COVID-19 vaccines.", "keywords": [ "2019-nCoV", "Basic Science", "COVID-19 vaccine", "Coronavirus", "Medical", "Misinformation", "Vaccines", "combat", "digital", "infectious disease treatment", "social media", "tool" ], "approved": true } }, { "type": "Grant", "id": "10500", "attributes": { "award_id": "75N93022C00048-0-9999-1", "title": "SYSTEMATIC UNDERSTANDING AND ELIMINATION OF MISINFORMATION ONLINE", "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": [], "start_date": "2022-08-08", "end_date": "2023-08-07", "award_amount": 299964, "principal_investigator": { "id": 26506, "first_name": "JAMES", "last_name": "NOLAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1579, "ror": "", "name": "GRYPHON SCIENTIFIC, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "To develop digital tools to identify and combat malicious digital bots that spread misinformation about infectious disease treatments and vaccines, including COVID-19 vaccines.", "keywords": [ "2019-nCoV", "Basic Science", "COVID-19 vaccine", "Coronavirus", "Misinformation", "Vaccines", "combat", "digital", "infectious disease treatment", "tool" ], "approved": true } }, { "type": "Grant", "id": "10511", "attributes": { "award_id": "1U01CK000671-01", "title": "Midwest Virtual Laboratory of Pathogen Transmission in Healthcare Settings (MVL-PATHS)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-30", "end_date": "2025-09-29", "award_amount": 299761, "principal_investigator": { "id": 26517, "first_name": "Majid", "last_name": "Bani Yaghoub", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 753, "ror": "", "name": "UNIVERSITY OF MISSOURI KANSAS CITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Midwest Virtual Laboratory of Pathogen Transmission in Healthcare Settings (MVL-PATHS) Project Summary Antimicrobial Resistant (AMR) pathogens have become a significant public health threat. Also, the COVID-19 pandemic has further revealed disparities in healthcare settings. By developing and implementing novel mathematical and computation models, the long-term goals are to optimize AMR control and preventive interventions and to improve the health equity. The central hypothesis is that the outputs of mathematical and computation models will provide optimized and effective guidelines to reduce the threat of AMR pathogen spread and reduce health disparities in healthcare settings. The rationale underlying this project is to fill the critical gap in modeling workforce capacity and develop a new generation of mathematical models for healthcare research. The central hypothesis will be tested by pursuing three specific aims to develop and employ a, (i) One Health modeling approach to understand the source, distribution and spread of AMR Enterobacteriaceae with a focus on Extended- spectrum beta-lactamase (ESBL)-producing E. coli, (ii) a novel Real-Time modeling approach to identify AMR pathogen transmission by asymptomatic spreaders and contaminated medical devices in hospitals, (iii) a novel Agent-Based Nested modeling approach to identify the effects of caregivers as vectors of disease spread, and effects of limited staffing and specialized care on equitable quality of care in nursing homes. We will pursue these aims using an innovative combination of mathematical and computational modeling techniques. These include both recently developed techniques of including human behavior in models and more-established techniques that have been applied very little to the study of health equity and AMR pathogen spread. The workforce development objectives of this proposal are to (i) enhance mathematical and computational modeling research capabilities of the public health workforce and (ii) increase the number of junior modeling professionals that are trained and experienced in modeling transmission of pathogens in healthcare settings partly incorporated with health disparities. The expected outcomes of this work are the successful training of five predoctoral fellows and creating a virtual laboratory of enhanced mathematical models to identify strategies for reducing the threat AMR pathogen spread and reducing health disparities. The results will have an important positive impact immediately because the virtual laboratory can also be used by healthcare professionals to further investigate the drivers of disease spread and estimate the relative benefits of multiple control and prevention strategies in a timely and cost-effective manner. In addition, the research outputs of this project will expand and strengthen national one-health efforts to combat resistance and will have a direct impact on CDC and its public health partners’ ability to reduce the costs, morbidity and mortality of healthcare associated infections.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10513", "attributes": { "award_id": "1P01HD109907-01", "title": "Growing up in a digital world: A synergistic approach to understanding media use in children ages 1-8 years", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-09", "end_date": "2025-08-31", "award_amount": 103806, "principal_investigator": { "id": 26518, "first_name": "RACHEL F.", "last_name": "BARR", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Progress in understanding the effects of media exposure on child outcomes has been limited by the lack of large and representative longitudinal datasets, the difficulty of tracking quality of content in an ever-changing media environment, and the lack of a mechanism to rapidly share and analyze results in a theoretically driven manner. The overarching goal of this P01 proposal is to examine trajectories of media use - characterizing the context, content, and problematic uses of media - in a diverse group of 1200 children aged 1 to 7 years and examining temporal associations with emotion regulation and social competence using a cohort sequential design. The admin core will collate and integrate the Comprehensive Assessment of Family Media Exposure (CAFE) Toolkit data along with emotion regulation and social competence outcome data across the three studies. The CAFE Toolkit measures household media use through a web-based questionnaire, time-use diary, and passive-sensing app installed on family mobile devices (Barr et al., 2020; Radesky et al., 2020b). The administrative core will manage the data integration of the three longitudinal studies across the entire age range (1-3, 3-5, 5-7 year olds). Data collected from new cohorts will be compared to data collected before and during the COVID pandemic by the same research groups using the same CAFE Toolkit to examine how media exposure patterns varied as a function of the pandemic and how those experiences are related to socio- emotional outcomes. The P01 will also develop methods to increase the efficiency of coding the quality of media content, a bottleneck in the field. Finally, the data will be integrated, shared, visualized, and analyzed in a shared analytic Research Hub. 1", "keywords": [ "2 year old", "3 year old", "7 year old", "Address", "Age", "Books", "COVID-19 pandemic", "Child", "Code", "Complex", "Data", "Data Analyses", "Data Analytics", "Data Set", "Databases", "Ecology", "Environment", "Family", "Goals", "Grant", "Household", "Infrastructure", "Joints", "Libraries", "Longitudinal Studies", "Measures", "Methods", "Online Systems", "Outcome", "Pattern", "Postdoctoral Fellow", "Predisposition", "Process", "Psychosocial Stress", "Questionnaires", "Reproducibility", "Research", "Research Personnel", "Sampling", "Science", "Secure", "Source Code", "Stream", "Surveys", "Toddler", "United States National Institutes of Health", "Variant", "aged", "analytical tool", "cohort", "coronavirus disease", "data cleaning", "data integration", "data management", "data repository", "data reuse", "data sharing", "data streams", "data structure", "data visualization", "design", "diaries", "digital", "emotion regulation", "experience", "handheld mobile device", "indexing", "longitudinal dataset", "machine learning model", "open data", "open source", "pandemic disease", "repository", "response", "sharing platform", "social skills", "socioeconomics", "time use", "training opportunity" ], "approved": true } }, { "type": "Grant", "id": "10519", "attributes": { "award_id": "1R43IP001195-01", "title": "mRNA-BASED VACCINE AGAINST MULTIPLE COVID-19 VARIANTS", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-30", "end_date": "2023-09-29", "award_amount": 252010, "principal_investigator": { "id": 26528, "first_name": "Mohammed", "last_name": "Bouziane", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1937, "ror": "", "name": "SUNOMIX THERAPEUTICS", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Over the last 25 months humanity has been confronting COVID-19 pandemic caused by the new Corona Virus 2 (SARS-CoV-2) infection. Mutations and deletions often occur in the genome of SARS-CoV-2 (predominantly in the Spike protein) resulting in more transmissible and pathogenic “variants of concern” (VOCs). Our long- term goal is to develop a potent COVID-19 vaccine to stop/reduce SARS-CoV-2 infections and/or COVID-19 disease caused by multiple VOCs. Major gaps: Out of the 50 mutations that occur in the genome of OMICRON variant, 32 mutations are concentrated in the Spike protein sequence alone. Because most mutation and deletion that produced the 20 known VOCs are mostly concentrated on the Spike protein sequence, there is a risk that some of current COVID-19 sub-unit vaccines, that used mainly the Spike protein as antigen, fail to protect against future VOCs despite inducing strong virus-specific neutralizing antibodies. This emphasizes two major gaps in knowledge: The need to design alternative second-generation coronaviruses vaccines that (1) will include non- structural epitopes and antigens (Ags), other than the Spike protein; and (2) will incorporate conserved B and T cell epitopes to induce cell-mediated immune responses (in addition to humoral responses). Preliminary Results: We: (1) Identified potential human T cell target epitopes (the part of a virus antigens that the immune system recognizes) from the whole SARS-CoV-2 genome; and (2) Produced a first prototype multi-epitope COVID-mRNA vaccine candidate using the scalable and proven mRNAs vaccine platform, and (3) Generated a novel “humanized” susceptible HLA-DR/HLA-A*0201/hACE2 triple transgenic mouse model in which to test additional COVID-mRNA-based vaccine candidates. We hypothesize that one of our 5 COVID-19 vaccine candidates will protect “humanized” mice from infection and COVID-like disease caused by intranasal inoculation with SARS-CoV-2 a, b, g, d and Omicron VOCs. Our Specific Aims are: Aim 1: To construct 5 additional multi- epitopes COVID-mRNA-based vaccine candidates, that will incorporate conserved B and T cell epitopes from SARS-CoV-2 VOCs that circulate in the United Sates and other 200 other countries. Aim 2: To test in our novel “humanized” mouse model the safety, immunogenicity, and protective efficacy against SARS-CoV-2 a, b, g, d or Omicron VOCs of 5 multi-epitope COVID-mRNA vaccine candidates, delivered intranasally. The durability of protection and its correlation with blocking/neutralizing antibodies and the number and function of tissue-resident SARS-CoV-2-specific CD4+ and CD8+ TRM cells in the lungs and brains will be determined. If successful, the lead vaccine that protects against most VOCs, will be tested in non-human primate for safety (SBIR Phase II) and subsequently could be moved quickly into an FDA Phase 1 clinical trial.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10524", "attributes": { "award_id": "75N95022C00032-0-9999-1", "title": "ACTIV-1 TIME-LIMITED BIOSPECIMEN STORAGE AND RELATED SERVICES (COVID-19), OPTION QUANTITY 1 STATISTICAL SUPPORT, OPTION QUANTITY 2 BIOSPECIMEN ANALYSI", "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": [], "start_date": "2022-09-22", "end_date": "2025-09-21", "award_amount": 2701936, "principal_investigator": { "id": 26532, "first_name": "SANDRA", "last_name": "BUTLER", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1608, "ror": "", "name": "TECHNICAL RESOURCES INTERNATIONAL, INC.", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "CRO Assistance for Long-term Storage, Distribution and Analysis of Biospecimens from the ACTIV-1 Master Protocol, an adaptive Phase 3 clinical trial to evaluate the safety and efficacy of three immune modulator drugs in hospitalized adults with COVID-19.", "keywords": [ "Adult", "COVID-19", "Immunomodulators", "Pharmaceutical Preparations", "Phase III Clinical Trials", "Protocols documentation", "Safety", "Services", "Time" ], "approved": true } }, { "type": "Grant", "id": "10536", "attributes": { "award_id": "263201800029I-0-759802200012-1", "title": "UNEQUAL TREATMENT REVISITED: THE CURRENT STATE OF RACIAL AND ETHNIC DISPARITIES IN HEALTH CARE", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)", "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)", "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-28", "end_date": "2024-09-27", "award_amount": 125000, "principal_investigator": { "id": 26548, "first_name": "ROBERT", "last_name": "DAY", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The Institute of Medicine’s (IOM, 2003) [Now the National Academy of Medicine as one of the National Academies of Sciences, Engineering, and Medicine, NASEM] groundbreaking report “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care” documented differences in the quality of healthcare services received by people from racial and ethnic minority groups, highlighting the roles that racial stratification and social inequities play in health outcomes. Published almost 20 years after the landmark Malone-Heckler report, Unequal Treatment provided compelling models and evidence demonstrating how the health care system operates on multiple levels to create, sustain, and increase racial and ethnic health disparities – emphasizing the contributions of factors beyond the control of the individual patient. Reviewing evidence from the 2003 report and data generated subsequently, NASEM will convene an ad hoc expert committee to examine the current state of racial and ethnic disparities in U.S. healthcare. Congress commissioned the IOM in 1999 to study the root causes of racial and ethnic health disparities due to the growing concern around people from racial and ethnic minority groups and people experiencing poverty becoming a “permanent health care underclass.” Due to the historical marginalization of these populations in the healthcare system, high rates of being uninsured or underinsured, along with high health care costs driving differential access, utilization and quality of care, the IOM sought to illuminate how and why key factors impacting healthcare access, utilization and quality of care contributed to health disparities. The foci were two levels of the healthcare system that were hypothesized to contribute significantly to racial and ethnic health disparities. They first examined the operation of healthcare systems and the legal and regulatory climate in which health systems function, providing more nuanced explanations that moved beyond attributing health disparities to differential healthcare access. The second focused on understanding discriminatory practices at the patient, clinician, and health system levels – measured by bias, stereotyping, and clinician/patient concordance – as causes of racial and ethnic health disparities. The report provided actionable recommendations for evidenced-based targeted interventions that could be implemented over time to improve quality of care and reduce racial and ethnic healthcare disparities. The major findings from the IOM report reinforced that healthcare system limitations had particularly negative implications for the quality of care received by Black/African American persons and certain Hispanic/Latino persons based on their birthplace or English language fluency. However, most of the available data at the time was available for Black/African American persons and there was limited information in the IOM report on other minoritized populations. Among other findings, the report concluded that: • Minoritized racial and ethnic patients often receive a lower quality of care and less intensity of indicated treatment and diagnostic services across a wide range of procedures and disease areas. • Insurance status is a key predictor of the quality of care that minoritized racial and ethnic groups receive since they are disproportionately represented in the Medicaid and dual-eligible Medicare categories and no health insurance; yet when insurance status is controlled, race and ethnicity remain significant predictors of quality of care. • Within the clinical encounter, minoritized patients may perceive both overt, as well as subtle forms of discrimination when seeking care. Bias, stereotyping, prejudice, and communication barriers on the part of clinicians and other healthcare staff may be contributory factors to racial and ethnic disparities in healthcare. • Limited assistance with professional interpretation services is available to patients with limited English proficiency, which has negative implications for the clinical encounter. • Sociocultural differences between patient and clinician influence communication and clinical decision making; thus, ineffective communication during the medical encounter may lead to patient dissatisfaction, non-adherence, poorer health outcomes, and subsequently, racial and ethnic disparities in healthcare. • A significant body of literature defines and supports the importance of cross-cultural education in the training of health professionals. Despite several approaches and various opportunities for integration, curricula in this area have been implemented to a modest degree in undergraduate, graduate, and continuing education of health professions. • Medical graduates who identify with an underrepresented minority group made up about 14% in 2019-2020, with 7% being African American, 6% Latino/a and 1% American Indian or Alaska Native and Native Hawaiian or Pacific Islander. The 22% of medical graduates who identify as Asians, include Southeast Asians who are also underrepresented. • More information is needed on the potential impacts of medical care delivered in the context of cultural and linguistic concordance between clinicians and their patients. These would include efforts to evaluate the role of physicians from underrepresented populations and that of international medical graduates and minoritized racial and ethnic populations, and specifically the extent to which this contributes to healthcare disparities. Along with identifying key areas of healthcare that create and sustain racial and ethnic disparities, the IOM report identified areas needed for further research and suggested several intervention strategies to eliminate disparities in quality of care and improve population health. These recommendations included: • Develop a better understanding of the relative contribution of patient, clinician, and institutional characteristics to healthcare disparities. • Further illuminate clinical decision-making, heuristics applied in diagnostic evaluation, and how patients' race, ethnicity, gender, English language fluency, and social class may influence these decisions. • Assess the relative contributions of clinician biases, stereotyping, prejudice, and uncertainty in producing racial and ethnic disparities in diagnosis, treatment, and outcomes of care. • Investigate the roles of non-physician healthcare professionals, including nurses, physician assistants, occupational and rehabilitation therapists, mental health professionals (including psychologists, social workers, and marital and family therapists), pharmacists, allied health professionals, as well as medical assistants, administrative, and laboratory staff in contributing to healthcare disparities. • Due to a paucity of research, assess healthcare disparities among Asian American, Native Hawaiian and Pacific Islander, American Indian and Alaska Native, and Hispanic or Latino populations and their subpopulations. • Assess the potential impacts of medical care delivered in the context of cultural and linguistic concordance between clinicians and their patients. These would include efforts to evaluate the role of physicians from underrepresented populations and that of international medical graduates and minoritized racial and ethnic populations, and specifically the extent to which this contributes to decreasing healthcare disparities. • Develop and test the utility of healthcare improvement of patient-based measures of (1) trust in clinicians and systems and (2) exposure to discriminatory practices by clinicians or systems. • Develop methods for monitoring progress toward reducing and ultimately eliminating racial and ethnic disparities in healthcare. • Understand the relationship between healthcare disparities and the health gap between racial and ethnic minority and White patients stratified by educational attainment. While the IOM report provided the foundational evidence base necessary for subsequent studies to address how healthcare related factors significantly contribute to disparities in healthcare quality for minoritized racial and ethnic persons and the approaches needed to address them, health disparities persist and, in many conditions, continue to widen. It has been 20 years since the publication of the IOM report, and factors outside of the control of the individual continue to play a significant role in disparate health outcomes. Needed is an understanding of the aspects of healthcare quality identified in the IOM report which have shown improvement, promise, or worsened. For example, a significant advancement in health care is the Patient Protection and Affordable Care Act of 2010 (ACA) which has increased insurance coverage for 20 million U.S. residents, reduced the insurance gap across all racial and ethnic groups in the U.S. and completely eliminated the disparity for Asian Americans, Native Hawaiians and Pacific Islanders, but not for other racial groups. 4 Unfortunately, there remains a considerable segment of the population that lacks access to healthcare due to lack of health insurance. The lack of insurance is most notable for Latino/Hispanic populations 18 to 64 years of age. Further, even among insured populations, numerous adverse social determinants of health—such as lack of transportation and paid sick leave—may impede access to care for marginalized groups. In addition, demographic shifts in the population and public health emergencies such as the COVID-19 pandemic have exacerbated racial and ethnic health disparities in all aspects of healthcare and health outcomes. These factors must be taken into consideration when assessing the current disparities landscape. Advancing the work of the previous IOM report will include a review of the state of racial and ethnic disparities in quality of care, access, and utilization, and expand to examine community and population level factors that operate to influence healthcare disparities. Current evidence suggests that the digital divide has hampered the potential of health information technology to expand access to healthcare for socioeconomically disadvantaged groups and racial and ethnic minority persons. 5 For example, in a study that assessed geographic and racial and ethnic disparities in access to care, Mantri & Mitchell (2021) found that with the shift to virtual care due to the COVID-19 pandemic, visits among Black/African American individuals was cut in half relative to pre-pandemic utilization. 6 Other research has also found that the COVID-19 pandemic has had an adverse impact on healthcare utilization due to limited telemedicine adoption7 and increased racial inequities in the quality and intensity of care. 8 Thus, it is important that healthcare systems emphasize access to high quality of care for all, strengthen preventive health care approaches, address social needs as part of healthcare delivery, and diversify the healthcare workforce to more closely reflect the demographic composition of the patient population.", "keywords": [ "Academy", "Acute", "Address", "Affect", "Affordable Care Act", "African American", "African American population", "Age-Years", "Alaska Native", "Ambulatory Care", "American Indians", "Area", "Asian Americans", "Asian population", "Automobile Driving", "Black race", "Budgets", "COVID-19", "COVID-19 pandemic", "Caring", "Categories", "Characteristics", "Chronic", "Climate", "Clinical", "Communication", "Communication Barriers", "Communities", "Congresses", "Consensus", "Continuing Education", "Country", "Data", "Decision Making", "Diagnosis", "Diagnostic", "Diagnostic Services", "Discrimination", "Disease", "Economically Deprived Population", "Education", "Educational Curriculum", "Elderly", "Emergency Care", "Emergency Situation", "Engineering", "English Language", "Ethnic Origin", "Ethnic group", "Evaluation", "Exposure to", "Family", "Focus Groups", "Foundations", "Gender", "Geographic Factor", "Geography", "Graduate Education", "Grant", "Health", "Health Care Costs", "Health Insurance", "Health Occupations", "Health Policy", "Health Professional", "Health Services Accessibility", "Health Services Research", "Health system", "Healthcare", "Healthcare Systems", "Hispanic", "Hispanic Populations", "Hospitals", "Individual", "Inequality", "Infrastructure Activities", "Institute of Medicine (U.S.)", "Insurance", "Insurance Coverage", "International", "Internet", "Intervention", "Knowledge", "Laboratories", "Latino", "Latino Population", "Laws", "Lead", "Legal", "Limited English Proficiency", "Linguistics", "Literature", "Longevity", "Measures", "Medicaid", "Medical", "Medicare", "Medicine", "Mental Health", "Methods", "Minority Groups", "Modeling", "Monitor", "Native Hawaiian or Other Pacific Islander", "Nurses", "Occupational", "Office Management", "Operating System", "Outcome", "Outpatients", "Pacific Island Americans", "Patients", "Persons", "Pharmacists", "Physician Assistants", "Physician&apos", "s Role", "Plant Roots", "Play", "Policies", "Policy Maker", "Population", "Poverty", "Prejudice", "Preventive healthcare", "Primary Health Care", "Procedures", "Psychologist", "Public Health", "Publications", "Publishing", "Quality of Care", "Race", "Recommendation", "Recording of previous events", "Reduce health disparities", "Regulation", "Rehabilitation therapy", "Reporting", "Research", "Research Personnel", "Resources", "Review Literature", "Role", "Series", "Services", "Sick Leave", "Social Class" ], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1397, "count": 13961 } } }