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Grant Details

Grant Number: 1R21CA245858-01A1 Interpret this number
Primary Investigator: Guo, Yi
Organization: University Of Florida
Project Title: Using Electronic Health Records From a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) Individuals
Fiscal Year: 2020


Abstract

ABSTRACT Transgender and gender nonconforming (TGNC) people face a disproportionate burden of adverse health outcomes. Although there is a growing body of literature on the unique health issues among TGNC populations, they remain severely underserved as existing data on TGNC health are scarce. Under-reporting is common due to issues related to social and economic marginalization, stigma, and discrimination, leading to challenges in obtaining population-based estimates since TGNC individuals are often unwilling to self-identify and reluctant to participate in traditional surveys. Further, past TGNC research has primarily focused on mental health, substance use and abuse, and sexual transmitted infections and diseases. There is limited data available on age-related chronic conditions such as cancer, the second leading cause of death in the United States. Nonetheless, cancer is one of the top research priorities among the TGNC population. With a rapidly growing aging TGNC population, there is an urgent need to characterize the cancer burden among these individuals and understand how cancer impact them differentially compared to non-TGNC individuals. On the other hand, rapid adoption of electronic health record (EHR) systems has made longitudinal clinical data available for research. EHRs contain not only important structured data, such as demographics, diagnoses, procedures, and medications, but also unstructured clinical narratives such as physician’s notes. More than 80 percent of the clinical information is documented in clinical narratives, which contain more detailed patient information including gender identity and cancer risk factors. Motivated by these observations and built upon our previous studies on 1) the adequacy of TGNC gender identity terms, 2) clinical natural language processing methods for information extraction, and 3) EHR-based cohort studies, we propose to conduct a population-based cohort analysis to examine the cancer burden and risk factors among TGNC people using a unique data source from a large network of EHRs—OneFlorida, one of the 13 PCORI-funded clinical data research networks (CDRNs) contributing to the PCORnet. Using both structured and unstructured OneFlorida data, we will first develop computable phenotypes to identify TGNC individuals and subsequently evaluate their cancer risk. Our research is significant because: 1) no population-based cohort studies on cancer risk have been conducted among the TGNC population. Our results will support the development of tailored, evidence- based cancer screening programs for TGNC people; 2) our research will create a cohort of TGNC people that can be not only tracked longitudinally in EHR but also recruited for future clinical studies; and 3) working with a PCORnet CDRN makes our analysis framework generalizable to the overall PCORNet. Overall, the proposed research will advance our knowledge in cancer among the aging TGNC population.



Publications

Comparing the downstream costs and healthcare utilization associated with the use of low-dose computed tomography (LDCT) in lung cancer screening in patients with and without alzheimer's disease and related dementias (ADRD).
Authors: Zhang Y. , Bian J. , Huo J. , Yang S. , Guo Y. , Shao H. .
Source: Current medical research and opinion, 2021 Oct; 37(10), p. 1731-1737.
EPub date: 2021-07-26.
PMID: 34252317
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Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections.
Authors: Jun I. , Rich S.N. , Chen Z. , Bian J. , Prosperi M. .
Source: International journal of medical informatics, 2021 09; 153, p. 104531.
EPub date: 2021-07-16.
PMID: 34332468
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The role of sex and rurality in cancer fatalistic beliefs and cancer screening utilization in Florida.
Authors: Guo Y. , Szurek S.M. , Bian J. , Braithwaite D. , Licht J.D. , Shenkman E.A. .
Source: Cancer medicine, 2021 Sep; 10(17), p. 6048-6057.
EPub date: 2021-07-13.
PMID: 34254469
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The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.
Authors: Guo Y. , Zhang Y. , Lyu T. , Prosperi M. , Wang F. , Xu H. , Bian J. .
Source: Journal of the American Medical Informatics Association : JAMIA, 2021-08-13; 28(9), p. 2050-2067.
PMID: 34151987
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Deep propensity network using a sparse autoencoder for estimation of treatment effects.
Authors: Ghosh S. , Bian J. , Guo Y. , Prosperi M. .
Source: Journal of the American Medical Informatics Association : JAMIA, 2021-06-12; 28(6), p. 1197-1206.
PMID: 33594415
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The association between cognitive impairment and breast and colorectal cancer screening utilization.
Authors: Yang S. , Bian J. , George T.J. , Daily K. , Zhang D. , Braithwaite D. , Guo Y. .
Source: BMC cancer, 2021-05-12; 21(1), p. 539.
EPub date: 2021-05-12.
PMID: 33975576
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Applications of artificial intelligence in drug development using real-world data.
Authors: Chen Z. , Liu X. , Hogan W. , Shenkman E. , Bian J. .
Source: Drug discovery today, 2021 05; 26(5), p. 1256-1264.
EPub date: 2020-12-24.
PMID: 33358699
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Oral cancer knowledge and screening behavior among smokers and non-smokers in rural communities.
Authors: Wong T.J. , Li Q. , Dodd V. , Wang W. , Bian J. , Guo Y. .
Source: BMC cancer, 2021-04-20; 21(1), p. 430.
EPub date: 2021-04-20.
PMID: 33879128
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Geographic Variation in Knowledge of Palliative Care Among US Adults: Findings From 2018 Health Information National Trends Survey.
Authors: Chen G. , Hong Y.R. , Wilkie D.J. , Kittleson S. , Huo J. , Bian J. .
Source: The American journal of hospice & palliative care, 2021 Mar; 38(3), p. 291-299.
EPub date: 2020-08-06.
PMID: 32757758
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International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records.
Authors: Guo Y. , Chen Z. , Xu K. , George T.J. , Wu Y. , Hogan W. , Shenkman E.A. , Bian J. .
Source: Medicine, 2020-12-24; 99(52), p. e23818.
PMID: 33350768
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Statin Use for Atherosclerotic Cardiovascular Disease Prevention Among Sexual Minority Adults.
Authors: Guo Y. , Wheldon C.W. , Shao H. , Pepine C.J. , Handberg E.M. , Shenkman E.A. , Bian J. .
Source: Journal of the American Heart Association, 2020-12-15; 9(24), p. e018233.
EPub date: 2020-12-02.
PMID: 33317368
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An ontology-based documentation of data discovery and integration process in cancer outcomes research.
Authors: Zhang H. , Guo Y. , Prosperi M. , Bian J. .
Source: BMC medical informatics and decision making, 2020-12-14; 20(Suppl 4), p. 292.
EPub date: 2020-12-14.
PMID: 33317497
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Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.
Authors: Guo Y. , He X. , Lyu T. , Zhang H. , Wu Y. , Yang X. , Chen Z. , Markham M.J. , Modave F. , Xie M. , et al. .
Source: AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020; 2020, p. 514-523.
EPub date: 2021-01-25.
PMID: 33936425
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