Agreement between Self-Reported and Administrative Race and Ethnicity Data among Medicaid Enrollees in Minnesota

This study examines the association between educational loans and college graduation rates, with a focus on differences by race and ethnicity. Data come from the 1997 National Longitudinal Survey of Youth. Results from the event history analyses indicate that educational loans are positively related to college graduation rates, but only up to a point (about US$19,753). Although this nonlinear relationship holds true among White, Black, and Hispanic students, there are differences in the level of loans where its effect turns negative on graduate rates. There is little evidence overall that educational loans reduce racial and ethnic disparities in college graduation.

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Race and Ethnicity Data in Research

JAMA ◽

10.1001/jama.2018.21947 ◽

2019 ◽

Vol 321 (12) ◽

pp. 1217 ◽

Cited By ~ 2

Author(s):

John Heintzman ◽

Miguel Marino

Keyword(s):

Race And Ethnicity ◽

Ethnicity Data

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Improving Patient Race and Ethnicity Data Capture to Address Health Disparities: A Case Study From a Large Urban Health System

Cureus ◽

10.7759/cureus.20973 ◽

2022 ◽

Author(s):

Ruben D Vega Perez ◽

Lyndia Hayden ◽

Jefri Mesa ◽

Nina Bickell ◽

Pamela Abner ◽

...

Keyword(s):

Health Disparities ◽

Health System ◽

Urban Health ◽

Race And Ethnicity ◽

Data Capture ◽

Patient Race ◽

Ethnicity Data

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Reporting and Availability of COVID-19 Demographic Data by US Health Departments (April to October 2020): Observational Study

JMIR Public Health and Surveillance ◽

10.2196/24288 ◽

2021 ◽

Vol 7 (4) ◽

pp. e24288

Author(s):

Peace Ossom-Williamson ◽

Isaac Maximilian Williams ◽

Kukhyoung Kim ◽

Tiffany B Kindratt

Keyword(s):

Data Collection ◽

Race And Ethnicity ◽

Demographic Data ◽

Health Department ◽

Disease Spread ◽

Health Departments ◽

Specific Data ◽

Us States ◽

Ethnicity Data

Background There is an urgent need for consistent collection of demographic data on COVID-19 morbidity and mortality and sharing it with the public in open and accessible ways. Due to the lack of consistency in data reporting during the initial spread of COVID-19, the Equitable Data Collection and Disclosure on COVID-19 Act was introduced into the Congress that mandates collection and reporting of demographic COVID-19 data on testing, treatments, and deaths by age, sex, race and ethnicity, primary language, socioeconomic status, disability, and county. To our knowledge, no studies have evaluated how COVID-19 demographic data have been collected before and after the introduction of this legislation. Objective This study aimed to evaluate differences in reporting and public availability of COVID-19 demographic data by US state health departments and Washington, District of Columbia (DC) before (pre-Act), immediately after (post-Act), and 6 months after (6-month follow-up) the introduction of the Equitable Data Collection and Disclosure on COVID-19 Act in the Congress on April 21, 2020. Methods We reviewed health department websites of all 50 US states and Washington, DC (N=51). We evaluated how each state reported age, sex, and race and ethnicity data for all confirmed COVID-19 cases and deaths and how they made this data available (ie, charts and tables only or combined with dashboards and machine-actionable downloadable formats) at the three timepoints. Results We found statistically significant increases in the number of health departments reporting age-specific data for COVID-19 cases (P=.045) and resulting deaths (P=.002), sex-specific data for COVID-19 deaths (P=.003), and race- and ethnicity-specific data for confirmed cases (P=.003) and deaths (P=.005) post-Act and at the 6-month follow-up (P<.05 for all). The largest increases were race and ethnicity state data for confirmed cases (pre-Act: 18/51, 35%; post-Act: 31/51, 61%; 6-month follow-up: 46/51, 90%) and deaths due to COVID-19 (pre-Act: 13/51, 25%; post-Act: 25/51, 49%; and 6-month follow-up: 39/51, 76%). Although more health departments reported race and ethnicity data based on federal requirements (P<.001), over half (29/51, 56.9%) still did not report all racial and ethnic groups as per the Office of Management and Budget guidelines (pre-Act: 5/51, 10%; post-Act: 21/51, 41%; and 6-month follow-up: 27/51, 53%). The number of health departments that made COVID-19 data available for download significantly increased from 7 to 23 (P<.001) from our initial data collection (April 2020) to the 6-month follow-up, (October 2020). Conclusions Although the increased demand for disaggregation has improved public reporting of demographics across health departments, an urgent need persists for the introduced legislation to be passed by the Congress for the US states to consistently collect and make characteristics of COVID-19 cases, deaths, and vaccinations available in order to allocate resources to mitigate disease spread.

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Pediatric cranial deformations: demographic associations

Journal of Neurosurgery Pediatrics ◽

10.3171/2020.3.peds2085 ◽

2020 ◽

Vol 26 (4) ◽

pp. 415-420 ◽

Cited By ~ 1

Author(s):

Jarrett Foster ◽

Ranbir Ahluwalia ◽

Madeleine Sherburn ◽

Katherine Kelly ◽

Georgina E. Sellyn ◽

...

Keyword(s):

African American ◽

Latin American ◽

Race And Ethnicity ◽

Asymmetry Index ◽

Clinical Treatment ◽

Significant Difference ◽

The Mean ◽

Age Range ◽

Ethnicity Data ◽

Treatment Window

OBJECTIVENo study has established a relationship between cranial deformations and demographic factors. While the connection between the Back to Sleep campaign and cranial deformation has been outlined, considerations toward cultural or anthropological differences should also be investigated.METHODSThe authors conducted a retrospective review of 1499 patients (age range 2 months to less than 19 years) who presented for possible trauma in 2018 and had a negative CT scan. The cranial vault asymmetry index (CVAI) and cranial index (CI) were used to evaluate potential cranial deformations. The cohort was evaluated for differences between sex, race, and ethnicity among 1) all patients and 2) patients within the clinical treatment window (2–24 months of age). Patients categorized as “other” and those for whom data were missing were excluded from analysis.RESULTSIn the CVAI cohort with available data (n = 1499, although data were missing for each variable), 800 (56.7%) of 1411 patients were male, 1024 (79%) of 1304 patients were Caucasian, 253 (19.4%) of 1304 patients were African American, and 127 (10.3%) of 1236 patients were of Hispanic/Latin American descent. The mean CVAI values were significantly different between sex (p < 0.001) and race (p < 0.001). However, only race was associated with differences in positional posterior plagiocephaly (PPP) diagnosis (p < 0.001). There was no significant difference in CVAI measurements for ethnicity (p = 0.968). Of the 520 patients in the treatment window cohort, 307 (59%) were male. Of the 421 patients with data for race, 334 were Caucasian and 80 were African American; 47 of the 483 patients with ethnicity data were of Hispanic/Latin American descent. There were no differences between mean CVAI values for sex (p = 0.404) or ethnicity (p = 0.600). There were significant differences between the mean CVAI values for Caucasian and African American patients (p < 0.001) and rate of PPP diagnosis (p = 0.02). In the CI cohort with available data (n = 1429, although data were missing for each variable), 849 (56.8%) of 1494 patients were male, 1007 (67.4%) of 1283 were Caucasian, 248 (16.6%) of 1283 were African American, and 138 patients with ethnicity data (n = 1320) of Hispanic/Latin American descent. Within the clinical treatment window cohort with available data, 373 (59.2%) of 630 patients were male, 403 were Caucasian (81.9%), 84 were African American (17.1%), and 55 (10.5%) of 528 patients were of Hispanic/Latin American descent. The mean CI values were not significantly different between sexes (p = 0.450) in either cohort. However, there were significant differences between CI measurements for Caucasian and African American patients (p < 0.001) as well as patients of Hispanic/Latin American descent (p < 0.001) in both cohorts.CONCLUSIONSThe authors found no significant associations between cranial deformations and sex. However, significant differences exist between Caucasian and African American patients as well as patients with Hispanic/Latin American heritage. These findings suggest cultural or anthropological influences on defining skull deformations. Further investigation into the factors contributing to these differences should be undertaken.

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Environmental justice concerns and the proposed Atlantic Coast Pipeline route in North Carolina

10.3768/rtipress.2018.mr.0037.1803 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah Wraight ◽

Julia Hofmann ◽

Justine Allpress ◽

Brooks Depro

Keyword(s):

North Carolina ◽

Environmental Justice ◽

Race And Ethnicity ◽

Atlantic Coast ◽

Vulnerability Index ◽

Census Bureau ◽

Data Sets ◽

Social Vulnerability Index ◽

Pipeline Route ◽

Ethnicity Data

This report describes publicly available data sets and quantitative analysis that local communities can use to evaluate environmental justice concerns associated with pipeline projects. We applied these data and analytical methods to two counties in North Carolina (Northampton and Robeson counties) that would be affected by the proposed Atlantic Coast Pipeline (ACP). We compared demographic and vulnerability characteristics of census blocks, census block groups, and census tracts that lie within 1 mile of the proposed pipeline route with corresponding census geographies that lie outside of the 1-mile zone. Finally, we present results of a county-level analysis of race and ethnicity data for the entire North Carolina segment of the proposed ACP route. Statistical analyses of race and ethnicity data (US Census Bureau) and Social Vulnerability Index scores (University of South Carolina’s Hazards & Vulnerability Research Institute) yielded evidence of significant differences between the areas crossed by the pipeline and reference geographies. No significant differences were found in our analyses of household income and cancer risk data.

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FDA Issues Guidance on Race and Ethnicity Data

PsycEXTRA Dataset ◽

10.1037/e542762006-008 ◽

2003 ◽

Author(s):

Michelle Meadows

Keyword(s):

Race And Ethnicity ◽

Ethnicity Data

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Developing an Algorithm for Combining Race and Ethnicity Data Sources in the Veterans Health Administration

Military Medicine ◽

10.1093/milmed/usz322 ◽

2019 ◽

Vol 185 (3-4) ◽

pp. e495-e500 ◽

Cited By ~ 1

Author(s):

Susan E Hernandez ◽

Philip W Sylling ◽

Maria K Mor ◽

Michael J Fine ◽

Karin M Nelson ◽

...

Keyword(s):

Race And Ethnicity ◽

Minority Groups ◽

Veterans Health Administration ◽

Data Sources ◽

Veterans Health ◽

Health Administration ◽

Financial Barriers ◽

Predictive Values ◽

Combining Data ◽

Ethnicity Data

Abstract Introduction Racial/ethnic disparities exist in the Veterans Health Administration (VHA), despite financial barriers to care being largely mitigated and Veterans Administration’s (VA) organizational commitment to health equity. Accurately identifying minority veterans is critical to monitoring progress toward equity as the VHA treats an increasingly racially and ethnically diverse veteran population. Although the VHA’s completeness of race and ethnicity data is generally better than its public sector and private counterparts, the accuracy of the race and ethnicity in the various databases available to VHA is variable, as is the accuracy in identifying specific minority groups. The purpose of this article was to develop an algorithm for constructing race and ethnicity variables from data sources available to VHA researchers, to present demographic differences cross the data sources, and to apply the algorithm to one study year. Materials and Methods We used existing VHA survey data from the Survey of Healthcare Experiences of Patients (SHEP) and three commonly used administrative databases from 2003 to 2015: the VA Corporate Data Warehouse (CDW), VA Defense Identity Repository (VADIR), and Medicare. Using measures of agreement such as sensitivity, specificity, positive and negative predictive values, and Cohen kappa, we compared self-reported race and ethnicity from the SHEP and each of the other data sources. Based on these results, we propose an algorithm for combining data on race and ethnicity from these datasets. We included VHA patients who completed a SHEP and had race/ethnicity recorded in CDW, VADIR, and/or Medicare. Results Agreement between SHEP and other sources was high for Whites and Blacks and substantially lower for other minority groups. The CDW demonstrated better agreement than VADIR or Medicare. Conclusions We developed an algorithm of data source precedence in the VHA that improves the accuracy of the identification of historically under-identified minorities: (1) SHEP, (2) CDW, (3) Department of Defense’s VADIR, and (4) Medicare.

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