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Madlock-Brown C, Lee A, Seltzer J, Solomonides A, Mathews N, Phuong J, Weiskopf N, Adams WG, Lehmann H, Espinoza J. Racial Disparities in Diabetes Care and Outcomes for Patients with Visual Impairment: A Descriptive Analysis of the TriNetX Research Network. Res Sq 2024:rs.3.rs-3901158. [PMID: 38352357 PMCID: PMC10862972 DOI: 10.21203/rs.3.rs-3901158/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Background: This research delves into the confluence of racial disparities and health inequities among individuals with disabilities, with a focus on those contending with both diabetes and visual impairment. Methods: Utilizing data from the TriNetX Research Network, which includes electronic medical records of roughly 115 million patients from 83 anonymous healthcare organizations, this study employs a directed acyclic graph (DAG) to pinpoint confounders and augment interpretation. We identified patients with visual impairments using ICD-10 codes, deliberately excluding diabetes-related ophthalmology complications. Our approach involved multiple race-stratified analyses, comparing co-morbidities like chronic pulmonary disease in visually impaired patients against their counterparts. We assessed healthcare access disparities by examining the frequency of annual visits, instances of two or more A1c measurements, and glomerular filtration rate (GFR) measurements. Additionally, we evaluated diabetes outcomes by comparing the risk ratio of uncontrolled diabetes (A1c > 9.0) and chronic kidney disease in patients with and without visual impairments. Results: The incidence of diabetes was substantially higher (nearly double) in individuals with visual impairments across White, Asian, and African American populations. Higher rates of chronic kidney disease were observed in visually impaired individuals, with a risk ratio of 1.79 for African American, 2.27 for White, and non-significant for the Asian group. A statistically significant difference in the risk ratio for uncontrolled diabetes was found only in the White cohort (0.843). White individuals without visual impairments were more likely to receive two A1c tests, a trend not significant in other racial groups. African Americans with visual impairments had a higher rate of glomerular filtration rate testing. However, White individuals with visual impairments were less likely to undergo GFR testing, indicating a disparity in kidney health monitoring. This pattern of disparity was not observed in the Asian cohort. Conclusions: This study uncovers pronounced disparities in diabetes incidence and management among individuals with visual impairments, particularly among White, Asian, and African American groups. Our DAG analysis illuminates the intricate interplay between SDoH, healthcare access, and frequency of crucial diabetes monitoring practices, highlighting visual impairment as both a medical and social issue.
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Hill EL, Mehta HB, Sharma S, Mane K, Singh SK, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health 2023; 23:2103. [PMID: 37880596 PMCID: PMC10601201 DOI: 10.1186/s12889-023-16916-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/05/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine L Hill
- Department of Public Health Sciences, University of Rochester Medical Center, 265 Crittenden Boulevard Box 420644, Rochester, NY, 14642, USA.
| | - Hemalkumar B Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Suchetha Sharma
- School of Data Science, University of Virginia, 3 Elliewood Ave, Charlottesville, VA, 22903, USA
| | - Klint Mane
- Department of Economics, University of Rochester, 1232 Mount Hope Ave, Rochester, NY, 14620, USA
| | - Sharad Kumar Singh
- Goergen Institute for Data Science, University of Rochester, 1209 Wegmans Hall, Rochester, NY, 14627, USA
| | - Catherine Xie
- CMC BOX 275184, University of Rochester, 500 Joseph C. Wilson Blvd, Rochester, NY, 14627-5184, USA
| | - Emily Cathey
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Johanna Loomba
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, Medical Branch, University of Texas, 301 University Blvd, Galveston, TX, 77555-1148, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 50 N Dunlap St., Memphis, TN, 38103, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 930 Madison Avenue 6Th Floor, Memphis, TN, 38163, USA
| | - Donald Brown
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 151 Engineer's Way Olsson Hall Rm. 102E, PO Box 400747, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, 12800 East 19Th Avenue, Aurora, CO, 80045, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, 2024 E Monument St. , Baltimore, MD, 21287, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, East 17Th Place Campus Box C290, Aurora, CO, 1300180045, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, MART L7 081011794, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, 160 N Medical Drive, Chapel Hill, NC, 27599, USA
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
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Alshakhs M, Goedecke PJ, Bailey JE, Madlock-Brown C. Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study. BMC Med 2023; 21:399. [PMID: 37867193 PMCID: PMC10591380 DOI: 10.1186/s12916-023-03084-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations. METHODS We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45-64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information. RESULTS The mean ages were 55 (45-64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45-64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45-64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45-64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45-64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45-64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings. CONCLUSIONS Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.
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Affiliation(s)
- Manal Alshakhs
- Health Outcomes and Policy Program, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Patricia J Goedecke
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - James E Bailey
- Center for Health System Improvement, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Charisse Madlock-Brown
- Health Outcomes and Policy Program, University of Tennessee Health Science Center, Memphis, TN, USA.
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 66 North Pauline St. Rm 221, Memphis, TN, 38163, USA.
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Bhatia A, Preiss AJ, Xiao X, Brannock MD, Alexander GC, Chew RF, Fitzgerald M, Hill E, Kelly EP, Mehta HB, Madlock-Brown C, Wilkins KJ, Chute CG, Haendel M, Moffitt R, Pfaff ER. Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C. medRxiv 2023:2023.05.03.23289084. [PMID: 37205340 PMCID: PMC10187454 DOI: 10.1101/2023.05.03.23289084] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.
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Affiliation(s)
- Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Xuya Xiao
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - G Caleb Alexander
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Elaine Hill
- University of Rochester, Department of Public Health Sciences and Department of Economics, Rochester, NY, USA
| | | | - Hemalkumar B Mehta
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Kenneth J Wilkins
- National Institute of Diabetes & Digestive & Kidney Diseases, Office of the Director, National Institutes of Health, Bethesda, MD, USA
- F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Christopher G Chute
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, Hill E, Kelly E, Kostka K, Loomba J, McMurry JA, Wong R, Bennett TD, Moffitt R, Chute CG, Haendel M. Coding long COVID: characterizing a new disease through an ICD-10 lens. BMC Med 2023; 21:58. [PMID: 36793086 PMCID: PMC9931566 DOI: 10.1186/s12916-023-02737-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/13/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.
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Affiliation(s)
- Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | | | - John M Baratta
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hannah Davis
- Patient-Led Research Collaborative, New York, USA
| | | | | | - Elizabeth Kelly
- University of North Carolina at Chapel Hill, Chapel Hill, USA
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6
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Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Caufield JH, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023; 87:104413. [PMID: 36563487 PMCID: PMC9769411 DOI: 10.1016/j.ebiom.2022.104413] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.
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Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Elena Casiraghi
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Johanna J Loomba
- The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan Laraway
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Kenneth J Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Nariman Ammar
- Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - J Harry Caufield
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Julie A McMurry
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Richard Moffitt
- Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | - Kristin Kostka
- Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Christopher G Chute
- Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
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7
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Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, Hill E, Kelly L, Kostka K, Loomba J, McMurry JA, Wong R, Bennett TD, Moffitt R, Chute CG, Haendel M. Coding Long COVID: Characterizing a new disease through an ICD-10 lens. medRxiv 2022:2022.04.18.22273968. [PMID: 36093345 PMCID: PMC9460974 DOI: 10.1101/2022.04.18.22273968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code ( n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.
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Affiliation(s)
| | | | | | | | | | | | | | - Liz Kelly
- University of North Carolina at Chapel Hill
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8
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Hill E, Mehta H, Sharma S, Mane K, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program. medRxiv 2022:2022.08.15.22278603. [PMID: 36032983 PMCID: PMC9413724 DOI: 10.1101/2022.08.15.22278603] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). Objective To identify risk factors associated with PASC/long-COVID. Design Retrospective case-control study. Setting 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Patients 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system. Measurements Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine Hill
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Hemal Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Suchetha Sharma
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Klint Mane
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Catherine Xie
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Emily Cathey
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Johanna Loomba
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Donald Brown
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
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9
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Reese JT, Blau H, Bergquist T, Loomba JJ, Callahan T, Laraway B, Antonescu C, Casiraghi E, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. medRxiv 2022:2022.05.24.22275398. [PMID: 35665012 PMCID: PMC9164456 DOI: 10.1101/2022.05.24.22275398] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
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10
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Xu NY, Nguyen KT, DuBord AY, Klonoff DC, Goldman JM, Shah SN, Spanakis EK, Madlock-Brown C, Sarlati S, Rafiq A, Wirth A, Kerr D, Khanna R, Weinstein S, Espinoza J. The Launch of the iCoDE Standard Project. J Diabetes Sci Technol 2022; 16:887-895. [PMID: 35533135 PMCID: PMC9264445 DOI: 10.1177/19322968221093662] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The first meeting of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) project, organized by Diabetes Technology Society, took place virtually on January 27, 2022. METHODS Clinicians, government officials, data aggregators, attorneys, and standards experts spoke in panels and breakout groups. Three themes were covered: 1) why digital health data integration into the electronic health record (EHR) is needed, 2) what integrated continuously monitored glucose data will look like, and 3) how this process can be achieved in a way that will satisfy clinicians, healthcare organizations, and regulatory experts. RESULTS The meeting themes were addressed within eight sessions: 1) What Do Inpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 2) What Do Outpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 3) Why Are Data Standards and Guidances Useful?, 4) What Value Can Data Integration Services Add?, 5) What Are Examples of Successful Integration?, 6) Which Privacy, Security, and Regulatory Issues Must Be Addressed to Integrate CGM Data into the EHR?, 7) Breakout Group Discussions, and 8) Presentation of Breakout Group Ideas. CONCLUSIONS Creation of data standards and workflow guidance are necessary components of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) standard project. This meeting, which launched iCoDE, will be followed by a set of working group meetings intended to create the needed standard.
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Affiliation(s)
- Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | - David C. Klonoff
- University of California, San
Francisco, San Francisco, CA, USA
- Mills-Peninsula Medical Center, San
Mateo, CA, USA
| | | | | | - Elias K. Spanakis
- Baltimore VA Medical Center, Baltimore,
MD, USA
- University of Maryland, Baltimore, MD,
USA
| | | | - Siavash Sarlati
- University of California, San
Francisco, San Francisco, CA, USA
- Anthem, Inc, Indianapolis, IN,
USA
| | - Azhar Rafiq
- National Aeronautics and Space
Administration, Washington, DC, USA
| | | | | | - Raman Khanna
- University of California, San
Francisco, San Francisco, CA, USA
| | | | - Juan Espinoza
- Division of General Pediatrics,
Department of Pediatrics, Children’s Hospital Los Angeles, University of Southern
California, Los Angeles, CA, USA
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11
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Madlock-Brown C, Wilkens K, Weiskopf N, Cesare N, Bhattacharyya S, Riches NO, Espinoza J, Dorr D, Goetz K, Phuong J, Sule A, Kharrazi H, Liu F, Lemon C, Adams WG. Correction: Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC Public Health 2022; 22:1250. [PMID: 35751109 PMCID: PMC9229081 DOI: 10.1186/s12889-022-13562-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Charisse Madlock-Brown
- grid.267301.10000 0004 0386 9246Health Informatics and Information Management, University of Tennessee Health Science Center, 66 North Pauline St. rm 221, Memphis, TN 38163 USA ,grid.267301.10000 0004 0386 9246Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN USA
| | - Ken Wilkens
- grid.419635.c0000 0001 2203 7304National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD USA
| | - Nicole Weiskopf
- grid.5288.70000 0000 9758 5690Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR USA
| | - Nina Cesare
- grid.189504.10000 0004 1936 7558Biostatistics and Epidemiology Data Analytics Center, Boston University, Boston, MA USA
| | - Sharmodeep Bhattacharyya
- grid.4391.f0000 0001 2112 1969Department of Statistics, Oregon State University, Corvallis, OR USA
| | - Naomi O. Riches
- grid.223827.e0000 0001 2193 0096Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Juan Espinoza
- grid.239546.f0000 0001 2153 6013Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA USA
| | - David Dorr
- grid.5288.70000 0000 9758 5690Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR USA
| | - Kerry Goetz
- grid.280030.90000 0001 2150 6316National Eye Institute, Bethesda, MD USA
| | - Jimmy Phuong
- grid.34477.330000000122986657University of Washington Research Information Technologies, Seattle, WA USA ,grid.470890.2Harborview Injury Prevention Research Center, Seattle, WA USA
| | - Anupam Sule
- grid.416708.c0000 0004 0456 8226Internal Medicine, St Joseph Mercy Oakland Hospital, Pontiac, MI USA
| | - Hadi Kharrazi
- grid.21107.350000 0001 2171 9311Johns Hopkins School of Public Health, Baltimore, MD USA
| | - Feifan Liu
- grid.168645.80000 0001 0742 0364Chan Medical School, University of Massachusetts, Worcester, MA USA
| | - Cindy Lemon
- grid.267301.10000 0004 0386 9246Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN USA
| | - William G. Adams
- grid.189504.10000 0004 1936 7558Boston Medical Center/Boston University School of Medicine, Boston, MA USA
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12
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Phuong J, Hong S, Palchuk MB, Espinoza J, Meeker D, Dorr DA, Lozinski G, Madlock-Brown C, Adams WG. Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research. AMIA Jt Summits Transl Sci Proc 2022; 2022:396-405. [PMID: 35854720 PMCID: PMC9285174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.
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Affiliation(s)
- Jimmy Phuong
- Division of Biomedical and Health Informatics, UW Medicine, Seattle, Washington
- University of Washington Medicine Research IT, Seattle, Washington
| | - Stephanie Hong
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA
| | - Daniella Meeker
- Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR
| | - Galina Lozinski
- Department of Pediatrics, Boston Medical Center/Boston University School of Medicine
| | - Charisse Madlock-Brown
- Tennessee Clinical and Translational Science Institute, University of Tennessee Health Science Center, Memphis, Tennessee
| | - William G Adams
- Department of Pediatrics, Boston Medical Center/Boston University School of Medicine
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13
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Cook L, Espinoza J, Weiskopf NG, Mathews N, Dorr DA, Gonzales KL, Wilcox A, Madlock-Brown C. Issues with Variability in EHR Data About Race and Ethnicity: A Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave (Preprint). JMIR Med Inform 2022; 10:e39235. [PMID: 35917481 PMCID: PMC9490543 DOI: 10.2196/39235] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/21/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. Objective This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. Methods At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.” Results “No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. Conclusions Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.
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Affiliation(s)
- Lily Cook
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Nicole G Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Nisha Mathews
- College of Human Sciences and Humanities, University of Houston, Clear Lake-Pearland, TX, United States
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Kelly L Gonzales
- Citizen of the Cherokee Nation, Portland, OR, United States
- Joint School of Public Health, Oregon Health & Science University-Portland State University, Portland, OR, United States
- Founding Indigenous Member, BIPOC Decolonizing Data Council, Portland, OR, United States
- Indigenous Equity Institute, Portland, OR, United States
| | - Adam Wilcox
- Department of Medicine, Institute for Informatics, Washington University in St. Louis, St. Louis, MO, United States
| | - Charisse Madlock-Brown
- Tennessee Clinical and Translational Science Institute, University of Tennessee Health Science Center, Memphis, TN, United States
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14
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Madlock-Brown C, Wilkens K, Weiskopf N, Cesare N, Bhattacharyya S, Riches NO, Espinoza J, Dorr D, Goetz K, Phuong J, Sule A, Kharrazi H, Liu F, Lemon C, Adams WG. Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC Public Health 2022; 22:747. [PMID: 35421958 PMCID: PMC9008430 DOI: 10.1186/s12889-022-13168-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/28/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
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Affiliation(s)
- Charisse Madlock-Brown
- Health Informatics and Information Management, University of Tennessee Health Science Center, 66 North Pauline St. rm 221, Memphis, TN, 38163, USA.
- Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Ken Wilkens
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Nicole Weiskopf
- Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nina Cesare
- Biostatistics and Epidemiology Data Analytics Center, Boston University, Boston, MA, USA
| | | | - Naomi O Riches
- Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - David Dorr
- Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Jimmy Phuong
- University of Washington Research Information Technologies, Seattle, WA, USA
- Harborview Injury Prevention Research Center, Seattle, WA, USA
| | - Anupam Sule
- Internal Medicine, St Joseph Mercy Oakland Hospital, Pontiac, MI, USA
| | - Hadi Kharrazi
- Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Feifan Liu
- Chan Medical School, University of Massachusetts, Worcester, MA, USA
| | - Cindy Lemon
- Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA
| | - William G Adams
- Boston Medical Center/Boston University School of Medicine, Boston, MA, USA
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15
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Phuong J, Zampino E, Dobbins N, Espinoza J, Meeker D, Spratt H, Madlock-Brown C, Weiskopf NG, Wilcox A. Extracting Patient-level Social Determinants of Health into the OMOP Common Data Model. AMIA Annu Symp Proc 2022; 2021:989-998. [PMID: 35308947 PMCID: PMC8861735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Deficiencies in data sharing capabilities limit Social Determinants of Health (SDoH) analysis as part of COVID-19 research. The National COVID Cohort Collaborative (N3C) is an example of an Electronic Health Record (EHR) database of patients tested for COVID-19 that could benefit from a SDoH elements framework that captures various screening instruments in EHR data warehouse systems. This paper uses the University of Washington Enterprise Data Warehouse (a data contributor to N3C) to demonstrate how SDoH can be represented and managed to be made available within an OMOP common data model. We found that these data varied by type of social determinants data and where it was collected, in the time period that it was collected, and in how it was represented.
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Affiliation(s)
- Jimmy Phuong
- Division of Biomedical and Health Informatics, UW Medicine, Seattle, Washington
- University of Washington Medicine Research IT, Seattle, Washington
| | - Elizabeth Zampino
- Division of Biomedical and Health Informatics, UW Medicine, Seattle, Washington
- University of Washington Medicine Research IT, Seattle, Washington
| | - Nicholas Dobbins
- Division of Biomedical and Health Informatics, UW Medicine, Seattle, Washington
- University of Washington Medicine Research IT, Seattle, Washington
| | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA
| | - Daniella Meeker
- Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - Heidi Spratt
- Preventative Medicine and Population Health, University of Texas Medical Branch, Galveston, Texas
| | - Charisse Madlock-Brown
- Dept of Health Informatics and Information Management, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Nicole G Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, OHSU, Portland, Oregon
| | - Adam Wilcox
- Division of Biomedical and Health Informatics, UW Medicine, Seattle, Washington
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16
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Finocchio T, Surbhi S, Madlock-Brown C. Time to Development of Overt Diabetes and Macrovascular and Microvascular Complications Among Patients With Prediabetes: A Retrospective Cohort Study. Cureus 2021; 13:e20079. [PMID: 34987939 PMCID: PMC8719530 DOI: 10.7759/cureus.20079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2021] [Indexed: 11/15/2022] Open
Abstract
Objective In this study, we aimed to determine the effect of age, gender, race, and obesity on the development of overt diabetes and macro/microvascular events among patients with prediabetes. Methods This was a retrospective cohort study of patient records available through a national electronic health record (EHR) database from 2012 to 2017. Patients with prediabetes in the baseline year of 2012 were identified. Macro/microvascular events were defined as the diagnosis of myocardial infarction (MI), stroke, or chronic kidney disease (CKD). The effects of age, gender, race, and obesity on the incidence of diabetes and macro/microvascular events between 2013-2017 were assessed using the multivariate Cox proportional-hazards model. Results Among the total 5,230 patients with prediabetes in 2012, 16.7% developed overt diabetes, and 19.7% developed a macro/microvascular event. Elderly patients (HR: 2.96, 95% CI: 2.12-4.13), males (HR: 1.38, 95% CI: 1.20-1.59), and African-Americans (HR: 1.47, 95% CI: 1.26-1.73) were at a higher risk of experiencing a macro/microvascular event. Additionally, male gender (HR: 1.27, 95% CI: 1.11-1.46) and obesity (HR: 1.24, 95% CI: 1.08-1.43) were significant factors associated with the development of overt diabetes. Furthermore, when diabetes status was added as an interaction term to the Cox proportional-hazards model, no statistical difference was found with respect to any of the other independent variables. It can therefore be inferred that those with prediabetes and overt diabetes had a similar risk of developing macro/microvascular events. Conclusions Based on our findings, factors including advanced age, obesity, male gender, and African race significantly impact the progression to diabetes and associated macro/microvascular events.
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Affiliation(s)
- Tyler Finocchio
- Department of Pharmacy Services, Yale New Haven Hospital, New Haven, USA
| | - Satya Surbhi
- Department of General Internal Medicine, College of Medicine, The University of Tennessee Health Science Center, Memphis, USA
| | - Charisse Madlock-Brown
- Health Informatics and Information Management, The University of Tennessee Health Science Center, Memphis, USA
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McCormack LA, Madlock-Brown C. Social Determinant of Health Documentation Trends and Their Association with Emergency Department Admissions. AMIA Annu Symp Proc 2021; 2020:823-832. [PMID: 33936457 PMCID: PMC8075477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Research has shown that health outcomes are significantly driven by patient's social and economic needs and environment, commonly referred to as the social determinants of health (SDoH). Standardized documentation of social and economic needs in healthcare are underutilized. This study examines the prevalence of documented social and economic needs (Z-codes) in a nationwide inpatient database and the association with emergency department (ED) admissions. Multivariate logistic regression was used to assess the effect of social and economic Z-codes on hospital admission through the ED. Payer source, gender, age at admission, comorbidity count, and median ZIP code income quartile covariates were included in the logistic regression analyses. Patients with documented social and economic Z-codes were significantly more likely to be admitted through the ED than those without documented social and economic needs, after adjusting for covariates. Standardized and widespread collection of these valuable Z-codes within EHR systems or administrative claims databases can help with targeted resource allocation to alleviate possible barriers to care and mitigate ED utilization.
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Madlock-Brown C, Reynolds RB. Identifying obesity-related multimorbidity combinations in the United States. Clin Obes 2019; 9:e12336. [PMID: 31418172 DOI: 10.1111/cob.12336] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/17/2019] [Accepted: 07/16/2019] [Indexed: 02/04/2023]
Abstract
Interest in understanding the effects of multimorbidity on outcomes has increased in recent years. This paper presents the most common obesity-related groupings of multimorbidity in the United States. Using Cerner HealthFacts data, we applied the frequent pattern growth algorithm to identify prevalent multimorbidity groupings of 3 or more diseases (one being obesity) by race using a dataset of 574 172 patients with obesity from all over the United States. We set the minimum prevalence to 10% and identified groupings of ICD10-CM diagnoses that occur in our dataset at or above the minimum prevalence level. We provide binomial proportion confidence interval estimates to demonstrate the validity of the proportions. We performed g-test for independence to validate differences in prevalence by race. We found 18 multimorbidity combinations with prevalence higher than or equal to 10%. Our results indicate that there are multiple common multimorbidities groupings for patients with obesity. Each multimorbidity combination is composed of diseases from the following clinical categories: endocrine, nutritional and metabolic diseases; diseases of the circulatory system; diseases of the digestive system; diseases of the nervous system; and diseases of the musculoskeletal system and connective tissue. For each multimorbidity pattern, the prevalence was found to be significantly different by race according to the g-test with P-value < .001. Most frequent patterns include essential hypertension or disorder of lipid metabolism. This study identifies common groupings of multimorbidity. We believe our data can be useful for those developing integrated care plans, particularly for those serving diverse communities.
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Affiliation(s)
- Charisse Madlock-Brown
- Department of Health Informatics and Information Management, College of Health Professions, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Rebecca B Reynolds
- Department of Health Informatics and Information Management, College of Health Professions, University of Tennessee Health Science Center, Memphis, Tennessee
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Darby AB, Su Y, Reynolds RB, Madlock-Brown C. A Survey-based Study of Pharmacist Acceptance and Resistance to Health Information Technology. Perspect Health Inf Manag 2019; 16:1a. [PMID: 31019433 PMCID: PMC6462883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
PURPOSE Because user acceptance and resistance to the use of health information technology (HIT) affects system utilization and previous studies in this area have typically excluded pharmacists, this study specifically addresses the response of institutional pharmacists to HIT. METHODS A survey investigating pharmacists' responses to electronic medical record (EMR) system use was developed using questions modified from previously validated research. The survey was distributed electronically to the mailing list for pharmacy preceptors for the University of Tennessee College of Pharmacy. Descriptive statistics and univariate and multivariate analyses were used to analyze the collected data based on a previously validated dual-factor model. RESULTS Of the 96 responses from institutional pharmacists, 64 responses (66.7 percent) were complete and usable. Of the acceptance and resistance constructs evaluated, only attitude and perceived behavior control were found to be significantly associated with acceptance of use (p = .036 and p = .025, respectively), and only transition cost was found to be significantly associated with resistance to use (p = .018). System vendor and interface integration were also significantly associated with acceptance of use. These findings suggest that attitude, perceived behavior control, and transition costs may have the most impact on pharmacists' responses to the use of EMR systems. CONCLUSION It is reasonable for hospitals to focus efforts on specific factors influencing acceptance of and resistance to EMR use and, before a system is selected, to consider the effects of vendor selection and level of interface integration on acceptance of use.
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Affiliation(s)
| | - Yin Su
- The University of Tennessee Health Science Center in Memphis, TN
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