Journal of Racial and Ethnic Health Disparities最新文献

{"title":"Developing a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study.","authors":"Paul Rogers, Christine Merenda, Richardae Araojo, Christine Lee, Milena Lolic, Ying Zhang, Jessica Reese, Kimberly Malloy, Dong Wang, Wen Zou, Joshua Xu, Elisa Lee","doi":"10.1007/s40615-024-02261-0","DOIUrl":"https://doi.org/10.1007/s40615-024-02261-0","url":null,"abstract":"<p><strong>Background: </strong>The Charlson Comorbidity Index (CCI) is a frequently used mortality predictor based on a scoring system for the number and type of patient comorbidities health researchers have used since the late 1980s. The initial purpose of the CCI was to classify comorbid conditions, which could alter the risk of patient mortality within a 1-year time frame. However, the CCI may not accurately reflect risk among American Indians because they are a small proportion of the US population and possibly lack representation in the original patient cohort. A motivating factor in calibrating a CCI for American Indians is that this population, as a whole, experiences a greater burden of comorbidities, including diabetes mellitus, obesity, cancer, cardiovascular disease, and other chronic health conditions, than the rest of the US population.</p><p><strong>Methods: </strong>This study attempted to modify the CCI to be specific to the American Indian population utilizing the data from the still ongoing The Strong Heart Study (SHS) - a multi-center population-based longitudinal study of cardiovascular disease among American Indians. A 1-year survival analysis with mortality as the outcome was performed using the SHS morbidity and mortality surveillance data and assessing the impact of comorbidities in terms of hazard ratios with the training cohort. A Kaplan-Meier plot for a subset of the testing cohort was used to compare groups with selected mCCI-AI scores.</p><p><strong>Results: </strong>A total of 3038 Phase VI participants from the SHS comprised the study population for whom mortality and morbidity surveillance data were available through December 2019. The weights generated by the SHS participants for myocardial infarction, congestive heart failure, and high blood pressure were greater than Charlson's original weights. In addition, the weights for liver illness were equivalent to Charlson's severe form of the disease. Lung cancer had the greatest overall weight derived from a hazard ratio of 8.31.</p><p><strong>Conclusions: </strong>The mCCI-AI was a statistically significant predictor of 1-year mortality, classifying patients into different risk strata χ² (8, N = 1,245) = 30.56 (p = 0.0002). The mCCI-AI was able to discriminate between participants who died and those who survived 73% of the time.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in Black-White Disparities in HIV Diagnosis by Selected Characteristics, 2017‒2021-United States.","authors":"André Dailey, Zanetta Gant Sumner, Juliet Morales, Sue Reynolds, Shacara Johnson Lyons, Anna Satcher Johnson","doi":"10.1007/s40615-024-02269-6","DOIUrl":"https://doi.org/10.1007/s40615-024-02269-6","url":null,"abstract":"<p><strong>Objective(s): </strong>To identify trends in HIV diagnosis disparities among subpopulations of Black/African American and White persons by selected characteristics (i.e., sex assigned at birth, age group, and region of residence) in the United States during 2017‒2021.</p><p><strong>Methods: </strong>Four-year estimated annual percent change (EAPC) during 2017‒2021, 2020 was excluded due to the impact of COVID-19 on HIV diagnoses, was used to assess temporal trends in diagnosis rates and disparities by selected characteristics for Black and White persons aged ≥ 13 years with a diagnosis of HIV infection. Data reported through December 2022 to the National HIV Surveillance System (NHSS) were used.</p><p><strong>Results: </strong>Among 74,161 Black persons and 44,641 White persons with HIV diagnosed during 2017-2021, there was an overall increase in HIV diagnosis rates among White females (EAPC = 2.0; CI = 0.3, 3.8), White males aged 35‒44 years (EAPC = 1.8; CI = 0.2, 3.5) and whose infection was attributed to injection drug use (EAPC = 7.5; CI = 4.6, 10.4), White females aged 35‒44 years (EAPC = 5.3; CI = 1.9, 8.9) and residing in the Midwest (EAPC = 5.5; CI = 1.4, 9.7). Among both Black and White males, we observed increases in relative disparities in HIV diagnosis for males aged 13‒24 years (EAPC = 7.4; CI = 7.0, 7.7) and residing in the West (EAPC = 2.0; CI = 1.1, 2.9).</p><p><strong>Conclusions: </strong>Efforts should prioritize eliminating disparities in treatment and prevention services by taking a comprehensive approach and actively mitigating the social determinants contributing to HIV disparities.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers to Healthcare Access for Indigenous Communities: Insights from Healthcare Professionals.","authors":"Sdney Jia Eer Tew, Yi Ting Sim, Yee Sin Chong, Qi Xuan Ng, Ronald Fook Seng Lee, Wei Jin Wong, Shaun Wen Huey Lee","doi":"10.1007/s40615-024-02274-9","DOIUrl":"https://doi.org/10.1007/s40615-024-02274-9","url":null,"abstract":"<p><strong>Background: </strong>The indigenous people or 'Orang Asli' (OA) in Malaysia is a vulnerable group who lags behind in terms of socioeconomic, education and health. Despite numerous developments, health disparities still exist among the OA, resulting in varying health outcomes. This study aimed to identify barriers to providing healthcare to OA by healthcare professionals (HCPs) in Malaysia.</p><p><strong>Methods: </strong>Semi-structure interview was conducted with 20 HCPs who have experience working with OA communities. Data were analysed thematically using the Health Care Access Barriers model as a framework.</p><p><strong>Results: </strong>Our study identified three themes which improve or hinder access to health care among OA communities. The main perceived barriers were (1) structural barriers, (2) cognitive barriers and (3) financial barriers.</p><p><strong>Conclusion: </strong>Our study illuminates how financial, structural and cognitive factors intersect to affect healthcare access among OA communities. To narrow the health outcome disparities, future research must engage with OA communities to ensure culturally appropriate and sustainable healthcare delivery.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Black Birthing Experiences: A Systematic Review and Social-Ecological Analysis of Disparities in the United States.","authors":"Grace K Kyei, Evans F Kyei, Rockson Ansong, Hannah Durowaa Odei-Opoku","doi":"10.1007/s40615-024-02254-z","DOIUrl":"https://doi.org/10.1007/s40615-024-02254-z","url":null,"abstract":"<p><strong>Background: </strong>Black birthing people in the United States face disproportionately high risks and adverse experiences during childbirth compared to their White counterparts. These challenges are shaped by a complex interplay of factors across individual, interpersonal, organizational, community, and policy levels.</p><p><strong>Objective: </strong>This systematic review explores the lived experiences of Black birthing people in the United States, using the Social Ecological Model to identify and analyze factors contributing to disparities in Black birthing health experiences.</p><p><strong>Methods: </strong>Eighteen qualitative studies, published between 2017 and 2024 and encompassing a sample of 570 participants, were systematically reviewed. A comprehensive search was conducted across PubMed, CINAHL, PsycINFO, and Scopus databases between March and May 2024. Studies were selected based on stringent inclusion criteria, and thematic analysis was applied to identify recurring factors that shape Black birthing experiences in the United States.</p><p><strong>Results: </strong>Key themes include personal health beliefs, psychological stress, medical distrust, interactions with healthcare professionals, family and peer influences, and the pervasive effects of systemic racism and healthcare policies. Together, these factors contribute to ongoing disparities in Black birthing healthcare experiences for Black birthing people in the United States.</p><p><strong>Conclusion: </strong>The findings underscore the importance of culturally competent care, equitable healthcare policies, and strong community support systems in addressing the unique challenges faced by Black birthing people. Targeted interventions, policy reforms, and continued research are essential for improving Black birthing experiences and outcomes.</p><p><strong>Impact: </strong>This review provides crucial insights into the multifaceted challenges in Black birthing experience emphasizing the need for culturally sensitive practices, policy changes for equity, and strengthened community resources. These steps are vital for ensuring respectful, equitable, and supportive experiences for Black birthing people in the United States.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.","authors":"Syed Ali Haider, Sahar Borna, Cesar A Gomez-Cabello, Sophia M Pressman, Clifton R Haider, Antonio Jorge Forte","doi":"10.1007/s40615-024-02237-0","DOIUrl":"https://doi.org/10.1007/s40615-024-02237-0","url":null,"abstract":"<p><strong>Introduction: </strong>As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.</p><p><strong>Methods: </strong>Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer-reviewed articles in English from 2013 to 2023 that examined instances of racial bias perpetuated by AI in healthcare. Studies conducted outside of healthcare settings or that addressed biases other than racial, as well as letters, opinions were excluded. The risk of bias was identified using CASP criteria for reviews and the Modified Newcastle Scale for observational studies.</p><p><strong>Results: </strong>Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1272 articles were initially identified, from which 26 met eligibility criteria. Four articles were identified via snowballing, resulting in 30 articles in the analysis. Studies indicate a significant association between AI utilization and the exacerbation of racial disparities, especially in minority populations, including Blacks and Hispanics. Biased data, algorithm design, unfair deployment of algorithms, and historic/systemic inequities were identified as the causes. Study limitations stem from heterogeneity impeding broad comparisons and the preclusion of meta-analysis.</p><p><strong>Conclusion: </strong>To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications. Comprehensive bias detection tools and mitigation strategies, coupled with active supervision by physicians, are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mental Illness Stigma in Black, Latina/o, and Asian Americans.","authors":"Andrew M Subica, Bruce G Link","doi":"10.1007/s40615-024-02259-8","DOIUrl":"https://doi.org/10.1007/s40615-024-02259-8","url":null,"abstract":"<p><p>Mental illness stigma has significant psychiatric consequences and can impede mental health treatment seeking, especially among racial minority groups; who are understudied in stigma research and experience striking treatment disparities. Guided by a novel empirical model of racial minority stigma and treatment seeking, this study investigated stigma and its effects on treatment seeking in Black, Latina/o, and Asian American adults. Data were collected via national panel survey from 613 Black, Latina/o, and Asian American adults. Perceptions of mental illness including seriousness, treatability, causal attributions, desired social distancing, and perceived dangerousness were assessed. Data were analyzed and compared with a nationally representative sample of the U.S. public from the 2018 General Social Survey. Minority participants exhibited stronger mental illness stigma than the U.S. public, with Black, Latina/o, and Asian American participants largely perceiving mental illness as less serious, less treatable, and desiring greater social distance from individuals with major depression, who were perceived as potentially dangerous. Notably, different stigma components significantly associated with willingness to seek treatment differently across Black, Latina/o, and Asian American participants. Overall, study findings indicate that mental illness stigma is strong and associates with treatment seeking in Black, Latina/o, and Asian Americans, suggesting a need to develop culturally tailored interventions to reduce stigma and associated treatment utilization disparities in these underserved minority groups.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-Related Discrimination and Health Care Access among a Nationally Representative, Diverse Sample of US Adults.","authors":"Miciah J Wilkerson, Alexis L Green, Allana T Forde, Stephanie A Ponce, Anita L Stewart, Anna M Nápoles, Paula D Strassle","doi":"10.1007/s40615-024-02260-1","DOIUrl":"https://doi.org/10.1007/s40615-024-02260-1","url":null,"abstract":"<p><strong>Background: </strong>In the United States, COVID-related discrimination towards racial and ethnic minority populations is well documented; however, its impact on healthcare access during the pandemic has not been assessed.</p><p><strong>Methods: </strong>We used data from our nationally representative, online survey of 5,500 American Indian/Alaska Native (AIAN), Asian, Black, Native Hawaiian/Pacific Islander, Latino, White, and multiracial adults conducted between 12/2020-2/2021 (baseline) and 8/2021-9/2021 (6-month follow-up; 35.1% response rate). At baseline, participants were asked how often they experienced discriminatory behaviors \"because they think you might have COVID-19\" (modified Everyday Discrimination Scale). Participants were asked if they were unable to get needed health care (e.g., cancer screening), or COVID-19 testing at both time-points. Vaccine willingness was assessed at baseline and uptake at follow-up.</p><p><strong>Results: </strong>Experiencing COVID-related discrimination was associated with not being able to get health care at baseline (OR = 3.66, 95% CI = 2.91-4.59) and follow-up (OR = 1.86, 95% CI = 1.16-2.97) and not being able to get a COVID-19 test at baseline (OR = 2.11, 95% CI = 1.68-2.65) and follow-up (OR = 4.12, 95% CI = 2.20-7.72). Experiencing discrimination was also associated with being less likely to have received a COVID-19 vaccine (OR = 0.52, 95% CI = 0.30-0.90), despite individuals who experienced discrimination being more willing to vaccinate at baseline (OR = 1.56, 95% CI = 1.10-2.22).</p><p><strong>Conclusions: </strong>COVID-related discrimination was associated with an increased likelihood of being unable to get health care across all racial and ethnic populations, although associations were strongest among Asian, AIAN, and Latino adults. Healthcare providers should be aware of the impact of discrimination on healthcare utilization, delays, and health-seeking behaviors, especially among racial and ethnic minorities.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142836864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Racial and Ethnic Differences in Utilization of Medications for Obesity Management in a Nationally Representative Survey.","authors":"Kimberly Narain, Christopher Scannell","doi":"10.1007/s40615-024-02248-x","DOIUrl":"https://doi.org/10.1007/s40615-024-02248-x","url":null,"abstract":"<p><strong>Background: </strong>The burden of obesity falls disproportionately on some racial and ethnic minority groups.</p><p><strong>Objective: </strong>To assess for racial and ethnic differences in the utilization of obesity-management medications among clinically eligible individuals.</p><p><strong>Design: </strong>Medical Expenditure Panel Survey (2011-2016, 2018 and 2020) data and a cross-sectional study design was used to assess for racial and ethnic differences in obesity-management medication utilization. Descriptive statistics and multivariable logistic regression models were used to identify the association of race and ethnicity with obesity-management medication utilization. Adjusted models controlled for demographics, socioeconomic status, obesity class, diabetes status, number of chronic conditions, insurance status, and geographic region.</p><p><strong>Participants: </strong>Adults with a BMI ≥ 30 kg/m² and individuals with a BMI ≥ 27 kg/m² with ≥ 1 weight-related condition.</p><p><strong>Main measures: </strong>The primary outcome measure was utilization of an FDA-approved medication for obesity-management during the study period. The primary independent predictor was race and ethnicity. Separate indicator variables were created for each racial and ethnic group (Non-Hispanic Asian, Non-Hispanic Black, Hispanic, and Non-Hispanic White (reference group)).</p><p><strong>Key results: </strong>In adjusted analyses, Asian (aOR, 0.36; 95% CI, 0.16 to 0.77; P < 0.01), Black (aOR, 0.51; 95% CI, 0.39 to 0.68; P < 0.001) and Hispanic individuals (aOR, 0.70; 95% CI, 0.49 to 0.98; P = 0.04) had significantly lower odds of utilizing obesity-management medications compared to White individuals.</p><p><strong>Conclusions: </strong>The results of this study suggest that there are racial and ethnic disparities in the use of obesity-management medications.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142836875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mental Health Care Disparities Among US Pregnant Individuals in 2020-2021: A Cross-Sectional Study.","authors":"Julisa Tindall, Monique J Brown, Peiyin Hung","doi":"10.1007/s40615-024-02250-3","DOIUrl":"https://doi.org/10.1007/s40615-024-02250-3","url":null,"abstract":"<p><strong>Purpose: </strong>To examine maternal characteristics associated with perceived unmet mental health needs and mental health care settings, focusing on residential rurality and race/ethnicity.</p><p><strong>Methods: </strong>This cross-sectional study analyzed self-reported unmet mental health needs and mental health care settings among 1097 pregnant respondents in the 2020-2021 National Survey on Drug Use and Health, incorporating the complex sampling weights for national representativeness.</p><p><strong>Findings: </strong>Non-Hispanic Black pregnant individuals and those living in nonmetro rural areas reported lower odds of unmet mental health needs compared to those in large metro areas and non-Hispanic White individuals. Pregnant individuals in nonmetro rural areas and non-Hispanic other pregnant individuals also reported lower odds of utilizing virtual mental care services, while non-Hispanic other pregnant individuals were less likely to receive prescription medication than their non-Hispanic White counterparts.</p><p><strong>Conclusions: </strong>Disparities in mental health care access by rurality and race/ethnicity reveal increased barriers for nonmetro rural and minority pregnant populations, particularly regarding virtual and prescription-based care. The lower unmet health needs among Black pregnant individuals and those living in nonmetro rural areas may reflect adjusted expectations or reliance on informal support systems, emphasizing the need to understand these perceptions. COVID-19's impact on access patterns further highlights the need for more research on barriers to maternal mental health treatment. Tailored mental health interventions and policy reforms are needed to enhance accessible, culturally sensitive maternal mental health services across diverse communities.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142836979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating Racism: Is Use of the Vaginal Birth After Cesarean Calculator Associated with Inequity in Perinatal Service Delivery?","authors":"Nicholas Rubashkin, Ifeyinwa V Asiodu, Saraswathi Vedam, Carolyn Sufrin, Miriam Kuppermann, Vincanne Adams","doi":"10.1007/s40615-024-02233-4","DOIUrl":"https://doi.org/10.1007/s40615-024-02233-4","url":null,"abstract":"<p><strong>Objective: </strong>The clinical application of race-adjusted algorithms may perpetuate health inequities. We assessed the impact of the vaginal birth after cesarean (VBAC) calculator, which was revised in 2021 to address concerns about equity. The original algorithm factored race and ethnicity and gave lower VBAC probabilities to Black and Hispanic patients.</p><p><strong>Methods: </strong>From 2019 to 2020, we conducted a multi-site, ethnographic study consisting of interviews and audio recordings of 14 prenatal visits. We used grounded theory to describe the social processes of racialization.</p><p><strong>Findings: </strong>Across 4 sites, 12 obstetricians, 5 midwives, and 31 pregnant/postpartum patients participated. Seventy-four percent (N = 23) of the pregnant/postpartum individuals identified as racially minoritized, and the remaining 24% (N = 8) identified as White. We identified four processes that facilitated the \"automation\" of racism: adhering to strict cutoffs; the routine adoption of calculators; obfuscating the calculator; and the reflexive categorization of race and ethnicity. When clinicians adhered to strict cutoffs, they steered low-scoring Black and Hispanic patients toward repeat cesareans. If clinicians obfuscated the calculator, Black and Hispanic patients had to work to decode the role of race and ethnicity in their probabilities in order to pursue a VBAC. By reflexively categorizing race and ethnicity, the use of the calculator forced patients to choose a singular identity, even if it obscured the truth about their multi-faceted race or ethnicity.</p><p><strong>Conclusion: </strong>The VBAC calculator's inclusion of race and ethnicity helped to automate racism by coding race into institutional practices and care interactions. This resulted in some clinicians discouraging or prohibiting Black and Hispanic patients from attempting a VBAC.</p><p><strong>Significance: </strong>To date, no empiric study has examined whether the VBAC calculator produced inequities in access to VBAC services and reproduced racism in care. The VBAC calculator resulted in fewer VBAC attempts among racially minoritized patients, denying them the opportunity to undergo labor and a vaginal birthing experience.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}