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Banschbach KM, Singleton J, Wang X, Vora SS, Harris JG, Lytch A, Pan N, Klauss J, Fair D, Hammelev E, Gilbert M, Kreese C, Machado A, Tarczy-Hornoch P, Morgan EM. Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry. Front Pediatr 2024; 12:1430981. [PMID: 39114853 PMCID: PMC11303283 DOI: 10.3389/fped.2024.1430981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
Introduction Ensuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry. Methods This is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis. Results Across six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion. Conclusions About one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.
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Affiliation(s)
- Katelyn M. Banschbach
- Division of Pediatric Rheumatology, Seattle Children’s Hospital, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
| | - Jade Singleton
- Biostatistics Epidemiology and Analytics in Research (BEAR), Seattle Children’s Research Institute, Seattle, WA, United States
| | - Xing Wang
- Biostatistics Epidemiology and Analytics in Research (BEAR), Seattle Children’s Research Institute, Seattle, WA, United States
| | - Sheetal S. Vora
- Division of Pediatric Rheumatology, Department of Pediatrics, Atrium Health Levine Children’s Hospital and Wake Forest University School of Medicine, Charlotte, NC, United States
| | - Julia G. Harris
- Division of Pediatric Rheumatology, Department of Pediatrics, Children’s Mercy Kansas City and University of Missouri-Kansas City School of Medicine, Kansas, MO, United States
| | - Ashley Lytch
- Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas, MO, United States
| | - Nancy Pan
- Department of Pediatrics, Weill Medical College of Cornell University, New York, NY, United States
- Division of Pediatric Rheumatology, Department of Medicine, Hospital for Special Surgery, New York, NY, United States
| | - Julia Klauss
- Division of Pediatric Rheumatology, Department of Medicine, Hospital for Special Surgery, New York, NY, United States
| | - Danielle Fair
- Division of Pediatric Rheumatology, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Erin Hammelev
- Division of Pediatric Rheumatology, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Mileka Gilbert
- Division of Pediatric Rheumatology, Department of Pediatrics, Shawn Jenkins Children’s Hospital, Medical University of South Carolina, Charleston, SC, United States
| | - Connor Kreese
- Shawn Jenkins Children’s Hospital, Medical University of South Carolina, Charleston, SC, United States
| | - Ashley Machado
- Division of Pediatric Rheumatology, Department of Pediatrics, Northwell Health, Cohen Children’s Medical Center, New York, NY, United States
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics and Medial Education, University of Washington, Seattle, WA, United States
- Division of Neonatology Department of Pediatrics, University of Washington, Seattle, WA, United States
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Esi M. Morgan
- Division of Pediatric Rheumatology, Seattle Children’s Hospital, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
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Chin MK, Đoàn LN, Russo RG, Roberts T, Persaud S, Huang E, Fu L, Kui KY, Kwon SC, Yi SS. Methods for retrospectively improving race/ethnicity data quality: a scoping review. Epidemiol Rev 2023; 45:127-139. [PMID: 37045807 DOI: 10.1093/epirev/mxad002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 02/27/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.
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Affiliation(s)
- Matthew K Chin
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Lan N Đoàn
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Rienna G Russo
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Timothy Roberts
- NYU Langone Health Sciences Library, NYU Grossman School of Medicine New York, NY 10016, United States
| | - Sonia Persaud
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
- Department of Health Policy and Management, CUNY School of Public Health & Health Policy, New York, NY 10027, United States
| | - Emily Huang
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Lauren Fu
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
- Georgetown University, Washington DC 20007, United States
| | - Kiran Y Kui
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
- Department of Epidemiology, Columbia Mailman School of Public Health, New York, NY 10032, United States
| | - Simona C Kwon
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Stella S Yi
- Section for Health Equity, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States
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Kader F, Ðoàn LN, Chin MK, Scherer M, Cárdenas L, Feng L, Leung V, Gundanna A, Lee M, Russo R, Ogedegbe OG, John I, Cho I, Kwon SC, Yi SS. IDEAL: A Community-Academic-Governmental Collaboration Toward Improving Evidence-Based Data Collection on Race and Ethnicity. Prev Chronic Dis 2023; 20:E90. [PMID: 37824700 PMCID: PMC10599325 DOI: 10.5888/pcd20.230029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023] Open
Affiliation(s)
- Farah Kader
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, NYU Langone Health, 180 Madison Ave, Floor 8, New York, NY 10016
| | - Lan N Ðoàn
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | - Matthew K Chin
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | - Maya Scherer
- Center for Evaluation and Applied Research, The New York Academy of Medicine, New York, New York
| | - Luisa Cárdenas
- Center for Evaluation and Applied Research, The New York Academy of Medicine, New York, New York
| | - Lloyd Feng
- Coalition for Asian American Children and Families, New York, New York
| | - Vanessa Leung
- Coalition for Asian American Children and Families, New York, New York
| | - Anita Gundanna
- Coalition for Asian American Children and Families, New York, New York
| | - Matthew Lee
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | - Rienna Russo
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | - Olugbenga G Ogedegbe
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | | | - Ilseung Cho
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Simona C Kwon
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
| | - Stella S Yi
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
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Liang PS, Kwon SC, Cho I, Trinh-Shevrin C, Yi S. Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion. Gastroenterology 2023; 164:320-324. [PMID: 36822735 PMCID: PMC10983115 DOI: 10.1053/j.gastro.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Peter S Liang
- Department of Medicine Department of Population Health, NYU Grossman School of Medicine, New York, New York; Department of Medicine, VA New York Harbor Health Care System, New York, New York
| | - Simona C Kwon
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Ilseung Cho
- Department of Medicine, NYU Grossman School of Medicine, New York, New York
| | - Chau Trinh-Shevrin
- Department of Medicine Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Stella Yi
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
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Liang PS, Kwon SC, Cho I, Trinh-Shevrin C, Yi S. Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion. Clin Gastroenterol Hepatol 2023; 21:567-571. [PMID: 36828600 DOI: 10.1016/j.cgh.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Indexed: 02/26/2023]
Affiliation(s)
- Peter S Liang
- Department of Medicine; Department of Population Health, NYU Grossman School of Medicine, New York, New York; Department of Medicine, VA New York Harbor Health Care System, New York, New York.
| | - Simona C Kwon
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Ilseung Cho
- Department of Medicine, NYU Grossman School of Medicine, New York, New York
| | - Chau Trinh-Shevrin
- Department of Medicine; Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Stella Yi
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
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