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Grabinski Z, Woo KM, Akindutire O, Dahn C, Nash L, Leybell I, Wang Y, Bayer D, Swartz J, Jamin C, Smith SW. Evaluation of a Structured Review Process for Emergency Department Return Visits with Admission. Jt Comm J Qual Patient Saf 2024:S1553-7250(24)00079-5. [PMID: 38653614 DOI: 10.1016/j.jcjq.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Review of emergency department (ED) revisits with admission allows the identification of improvement opportunities. Applying a health equity lens to revisits may highlight potential disparities in care transitions. Universal definitions or practicable frameworks for these assessments are lacking. The authors aimed to develop a structured methodology for this quality assurance (QA) process, with a layered equity analysis. METHODS The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher's exact tests. RESULTS The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education. CONCLUSION The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.
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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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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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Zhu C, Brown CT, Dadashova B, Ye X, Sohrabi S, Potts I. Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm. ACCIDENT; ANALYSIS AND PREVENTION 2023; 182:106964. [PMID: 36638723 DOI: 10.1016/j.aap.2023.106964] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/05/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver's age, victim's age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.
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Affiliation(s)
- Chunwu Zhu
- Texas A&M Transportation Institute (TTI), Texas A&M University, Texas, USA; Department of Landscape Architecture and Urban Planning, School of Architecture, Texas A&M University, Texas, USA.
| | | | - Bahar Dadashova
- Texas A&M Transportation Institute (TTI), Texas A&M University, Texas, USA.
| | - Xinyue Ye
- Texas A&M Transportation Institute (TTI), Texas A&M University, Texas, USA; Department of Landscape Architecture and Urban Planning, School of Architecture, Texas A&M University, Texas, USA
| | - Soheil Sohrabi
- Safe Transportation Research and Education Center (SafeTREC), University of California, Berkeley, California, USA
| | - Ingrid Potts
- Texas A&M Transportation Institute (TTI), Texas A&M University, Texas, USA
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Lombardi LR, Pfeiffer MR, Metzger KB, Myers RK, Curry AE. Improving identification of crash injuries: Statewide integration of hospital discharge and crash report data. TRAFFIC INJURY PREVENTION 2022; 23:S130-S136. [PMID: 35696334 PMCID: PMC9744954 DOI: 10.1080/15389588.2022.2083612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type. METHODS We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days. RESULTS In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists. CONCLUSIONS Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.
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Affiliation(s)
- Leah R. Lombardi
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Melissa R. Pfeiffer
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Kristina B. Metzger
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Rachel K. Myers
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
- Division of Emergency Medicine, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Allison E. Curry
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
- Division of Emergency Medicine, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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