As of 2023, across 70 measures assessing health and healthcare in the U.S., the Black, AI/AN, and Hispanic populations fare worse than the White population. The racial/ethnic disparity was largest comparing Black and White populations. The Black population fared worse than the White population across 55 health and healthcare measures, while they only fared better than the White population for 12 of them.

On the other hand, the Asian population did not fare worse than White people across most examined measures. Nonetheless, these measures cover aspects of health coverage, access, and use; health status, outcomes, and behaviors; and social determinants of health, yet more is needed to provide the full scope of healthcare disparities.

We analyzed satisfaction with care, out-of-pocket costs, and specialist access among community-dwelling Medicare Current Beneficiary Survey respondents, 2015–2019, in the 50 states and Washington, DC. For each measure, we constructed a binary indicator indicating very satisfied (vs. very dissatisfied to satisfied).;We used logistic regression to model outcomes as a function of Medicare Advantage - MA (vs. Traditional Medicare - TM) enrollment, respondent-reported race/ethnicity, and interactions of MA with race/ethnicity. Race/ethnicity was categorized as non-Hispanic Black, Hispanic, and non-Hispanic White. We adjusted for age, sex, education, income, tobacco use, chronic conditions, functional limitations, disability, and geographic factors. Racial/ethnic disparities reflect effects of structural factors that systematically disadvantage members of minoritized racial/ethnic groups. Because structural racism contributes to disparities in socioeconomic status (including income and education), we verified that our estimates did not change appreciably when we did not adjust for socioeconomic factors. ;Analyses were weighted by a composite of survey weights and propensity score weights to balance MA and TM populations within racial/ethnic groups. Separate analyses were conducted for beneficiaries with vs. without dual eligibility for full Medicaid.

We used SAS to process the data.

Few reports have suggested that non-Hispanic (NH) blacks may present with different symptoms for COVID-19 than NH-whites. The objective of this study was to investigate patterns in symptomatology and COVID-19 outcomes by race/ethnicity among adults in Arkansas. Data on COVID-19 symptoms were collected on day of testing, 7th and 14th day among participants at UAMS mobile testing units throughout the state of Arkansas. Diagnosis for SARS-CoV-2 infection was confirmed via nasopharyngeal swab and RT-PCR methods. Data analysis was conducted using Chi-square test and Poisson regression to assess the differences in characteristics by race/ethnicity. A total of 60,648 individuals were RT-PCR tested from March 29, 2020 through October 7, 2020. Among adults testing positive, except shortness of breath, Hispanics were more likely to report all symptoms than NH-whites or NH-blacks. NH-whites were more likely to report fever (19.6% vs. 16.6%), cough (27.5% vs. 26.1%), shortness of breath (13.6% vs. 9.6%), sore throat (16.7% vs. 10.7%), chills (12.5% vs. 11.8%), muscle pain (15.6% vs. 12.4%), and headache (20.3% vs. 17.8%). NH-blacks were more likely to report loss of taste/smell (10.9% vs. 10.6%). To conclude, we found differences in COVID-19 symptoms by race/ethnicity, with NH-blacks and Hispanics more often affected with specific or all symptoms, compared to NH-whites. Due to the cross-sectional study design, these findings do not necessarily reflect biological differences by race/ethnicity; however, they suggest that certain race/ethnicities may have underlying differences in health status that impact COVID-19 outcomes.

IntroductionEnsuring 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.MethodsThis 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.ResultsAcross 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.ConclusionsAbout 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.

Introduction: Early reports highlighted racial/ethnic disparities in the severity of COVID-19 seen across the USA; the extent to which these disparities have persisted over time remains unclear. Our research objective was to understand temporal trends in racial/ethnic variation in severity of COVID-19 illness presenting over time. Methods: We conducted a retrospective cohort analysis using longitudinal data from Cedars-Sinai Medical Center, a high-volume health system in Southern California. We studied patients admitted to the hospital with COVID-19 illness from 4 March 2020 through 5 December 2020. Our primary outcome was COVID-19 severity of illness among hospitalised patients, assessed by racial/ethnic group status. We defined overall illness severity as an ordinal outcome: hospitalisation but no intensive care unit (ICU) admission; admission to the ICU but no intubation; and intubation or death. Results: A total of 1584 patients with COVID-19 with available demographic and clinical data were included. Hispanic/Latinx compared with non-Hispanic white patients had higher odds of experiencing more severe illness among hospitalised patients (OR 2.28, 95% CI 1.62 to 3.22) and this disparity persisted over time. During the initial 2 months of the pandemic, non-Hispanic blacks were more likely to suffer severe illness than non-Hispanic whites (OR 2.02, 95% CI 1.07 to 3.78); this disparity improved by May, only to return later in the pandemic. Conclusion: In our patient sample, the severity of observed COVID-19 illness declined steadily over time, but these clinical improvements were not seen evenly across racial/ethnic groups; greater illness severity continues to be experienced among Hispanic/Latinx patients.

Background. Safety-net hospitals provide care for racially/ethnically diverse and disadvantaged urban populations. Their hospitalized patients with cirrhosis are relatively understudied and may be vulnerable to poor outcomes and racial/ethnic disparities. Aims. To examine the outcomes of patients with cirrhosis hospitalized at regionally diverse safety-net hospitals and the impact of race/ethnicity. Methods. A study of patients with cirrhosis hospitalized at 4 safety-net hospitals in 2012 was conducted. Demographic, clinical factors, and outcomes were compared between centers and racial/ethnic groups. Study endpoints included mortality and 30-day readmission. Results. In 2012, 733 of 1,212 patients with cirrhosis were hospitalized for liver-related indications (median age 55 years, 65% male). The cohort was racially diverse (43% White, 25% black, 22% Hispanic, 3% Asian) with cirrhosis related to alcohol and viral hepatitis in 635 (87%) patients. Patients were hospitalized mainly for ...

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study examines several explanations for the observed racial/ethnic disparities in drug arrests, the consequences of drug arrest on subsequent drug offending and social bonding, and whether these consequences vary by race/ethnicity. The study is a secondary analysis of the National Longitudinal Survey of Youth 1997 (NLSY97).

Distributed here are the codes used for the secondary analysis and the code to compile the datasets. Please refer to the codebook appendix for instructions on how to obtain all the data used in this study.

This study explores whether perinatal telehealth uptake has mitigated the pandemic's effects on disparities in maternal care access, quality, and outcomes by race, ethnicity, and rural or urban residence. Research to date has approached this question in several ways. First, researchers have utilized census data to assess whether community-wide broadband infrastructure exists to support the use of telehealth services in areas with high travel times to maternal care units. Findings suggest that socioeconomically disadvantaged communities face significant barriers to maternity care access, both with substantial travel burdens and inadequate digital access to facilitate telehealth services. Second, to examine maternal care quality, researchers have employed South Carolina hospital-based claims data and vital statistics to identify racial, ethnic, and urban/rural disparities in rates of cesarean delivery before and during the COVID-19 pandemic period. Results indicate that cesarean rates differed by rural vs. urban facility locations and racial and ethnic groups but observed disparities were not significantly exacerbated by the pandemic. Third, using South Carolina hospital-based claims data and COVID-19 testing data, researchers found significant racial, ethnic, and rural disparities in postpartum readmissions involving mental health and substance use disorders from childbirth discharge through one year postpartum during the COVID-19 pandemic. Finally, drawing on data from the National COVID Cohort Collaborative (N3C), research has shown that hybrid care increased substantially during the COVID-19 public health emergency, but pregnant people living in rural areas had lower levels of hybrid care than urban people, and individuals who belonged to racial and ethnic minority groups were more likely to have hybrid care than White individuals. Future research will investigate the impact of the COVID-19 pandemic and perinatal telehealth uptake on additional maternity care and birth outcomes by race, ethnicity, and urbanicity. The study also aims to assess how state-level telehealth policies relate to perinatal telehealth uptake by race, ethnicity, and urbanicity, and to develop a model to predict long-term changes in maternal care access, quality, outcomes, and expenditures, with and without state telehealth policies. The ICPSR provides variable-level metadata for the data associated with this study. The actual data may only be available from the Principal Investigator directly. The variable descriptions available through ICPSR also include information regarding the source of each variable listed, as does the Data Source field of these metadata.

A subset of the FY13 National Veteran Health Equity Report, filtered by race/ethnicity.

The National Veteran Health Equity Report details patterns and provides comparative rates of health conditions for vulnerable Veteran groups. Specifically, this report is designed to provide basic comparative information on the sociodemographics, utilization patterns and rates of diagnosed health conditions among the groups over which the VHA Office of Health Equity (OHE) has responsibility with respect to monitoring, evaluating and acting on identified disparities in access, use, care, quality and outcomes. The report allows the VA, Veterans, and stakeholders to monitor the care vulnerable Veterans receive and set goals for improving their care.

Usage

Researchers will need a DUA. There is no cost to reuse the dataset.

Collaboration Notes

I am open to new collaborations AND I am open to supporting a doctoral student

Start and End Dates of Data

2018-2019

Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown. Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk change across groups. Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites. Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection. Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277

This package contains replication data for: "Comparison of knowledge and intended behaviors following general COVID-19 public health messages and messages tailored for African American and Latinx communities: A randomized controlled trial." The data includes 1 raw dataset (except for removing a Zip code variable, as well as a free-response to prior medical conditions, for anonymity) containing data from one online Qualtrics survey that was conducted in 1 round with 15,475 observations from May 13, 2020 to May 24, 2020. The code, produced in R, contains both cleaning and analysis code. For further details on the data or how to run the code, please see the readme file. The abstract of the paper is as follows: Background: There is concern that the paucity of public health messages that directly address communities of color might contribute to racial ethnic disparities in COVID-19-related knowledge, behaviors, and outcomes. Objective: To determine if video public health messages differ in their influence, knowledge and intended behaviors of African American and Latinx individuals according to the race/ethnicity of the physician delivering the message and the content of the message. Design: Randomized controlled trial. Setting: United States May 13 2020-May 24 2020 Participants: 14,267 self-identified African American or Latinx adults recruited via Lucid survey platform. Intervention: Participants viewed 3 video messages about COVID-19 that varied by physician race/ethnicity, acknowledgement of racism/inequality, and community perceptions of mask-wearing. Measurements: Knowledge gaps (measured by lack of recognition of key COVID-19 symptoms, preventive behaviors or asymptomatic transmission) and intended behavior, measured by links demanded for prevention information.

Note: These datasets are part of our manuscript, which might be submitted to a double-anonymous review journal. We have to delete the information of all the authors at this point because reviews might check these datasets. We will add the information and affiliation of all authors once the manuscript is accepted.

This study aims to provide insight into the US Southwest social and environmental inequality problems by combining high-resolution ECOSTRESS data, including Land Surface Temperature (LST), Evaporative Stress Index (ESI), and actual Evapotranspiration (ETa) with sociodemographic data at the block group level acquired from US Census. ESI and ETa represent drought and consumptive water use, respectively. Further, disparities of environmental changes over the past two decades in connection with races/ethnicities are explored using Landsat-based LST and ET from 2000 to 2020 across major US Southwest cities in light of global climate changes. We narrow our investigations to the summer months, including June, July, and August, when environmental issues are more pronounced during the day, such as heat-related mortality and morbidity and higher water consumption.

This dataset covers major US Southwest cities. The dataset includes social-environmental data at the block level and remotely sensed environmental data. The description for each individual dataset is listed as follows,

T01(T01_dataset_meta_race_ethnicity_economy_env_southwest.csv): This dataset provides statistics on race/ethnicity, social economy, and arithmetic mean of environmental variables at the block group level.

T02: These datasets provide processed environmental data, including Daytime Land Surface Temperature (LST), Actual Evapotranspiration (ETa), and Evaporative Stress Index (ESI) images at 70m resolution.

T03: These datasets include annual Landsat-based summer LST, and ETa means at 30m resolution from 2000 to 2020. The images of LST and ETa in 2012 are missing because Landsat datasets in 2012 are unavailable. We collected all available Landsat images with cloud cover lower than 75% in the summer from 2000 to 2020 to produce the dataset. The high-resolution (30m) LST and ETa are processed on Google Earth Engine (GEE). There are a total of 8860 scenes (including Landsat 4, 5, 7, and 8) from 2000 to 2020 to cover all major US Southwest urban areas. We also include two sample images of 2020 LST and ETa for demonstration.

T04: These datasets contain LST and ETa Sen's slope results, which represent the median rate of change of LST and ETa from 2000 to 2020 on an annual basis.

T05: The boundary of the major US Southwest urban areas

The overall goal of this research project was to examine the impact that risk assessment has on the sentencing of racial minority youth and youth from disadvantaged neighborhoods. One of the main goals of correctional risk assessment is to reduce disparate outcomes for certain groups of youth in the juvenile justice system (e.g. Black youth). In practice, risk assessment is used with this goal in mind. However, there is very little research which shows whether or not risk assessment actually has its intended effects on sentencing. Therefore, this study set out to examine whether or not risk assessment reduces the sentencing gap seen in most research for minority youth and youth from disadvantaged neighborhoods. In addition, several other important research topics were explored to understand the role that race and socioeconomic disadvantage play in the juvenile justice system. These research topics included: (1) variation in the predictive validity of risk assessment across race, (2) variation in the predictive validity of risk assessment across neighborhood disadvantage, and (3) the moderating effects of race/gender and court dispositions on the predictive validity of risk assessment. To achieve the research goals in this study, data was collected from a large juvenile court in a Midwestern County. Information from 4,383 youth that came into contact with the court between January 2010 and December 2016 were included in the study. Data was collected that related to youth demographics, neighborhood characteristics in which youth lived, risk assessments data measured by the Ohio Youth Assessment System (OYAS), treatment programming received, court dispositions/sentencing, and recidivism.

Grounded in a strength-based (asset) model, this study explores the racial disparities in students’ learning and well-being during the pandemic. Linking the U.S. national/state databases of education and health, it examines whole-child outcomes and related factors—remote learning and protective community. It reveals race/ethnicity-stratified, state-level variations of learning and well-being losses in the midst of school accountability turnover. This data file includes aggregate state-level data derived from the NAEP and NSCH datasets, including all 50 U.S. states' pre-pandemic and post-pandemic measures of whole-child development outcomes (academic proficiency, socioemotional wellness, and physical health) as well as environmental conditions (remote learning and protective community) among school-age children. Methods To address the research questions, this study examines repeated cross-sectional datasets with nation/state-representative samples of school-age children. For academic achievement measures, the National Assessment of Educational Progress (NAEP) 2019 and 2022 datasets are used to assess nationally representative samples of 4th-grade and 8th-grade students’ achievement in reading and math (http://www.nces.ed.gov/nationsreportcard). In 2019, the NAEP samples included: 150,600 fourth graders from 8,300 schools and 143,100 eighth graders from 6,950 schools. In 2022, the NAEP samples included: (1) for reading, 108,200 fourth graders from 5,780 schools and 111,300 eighth graders from 5,190 schools; (2) for math, 116,200 fourth graders from 5,780 schools and 111,000 eighth graders from 5,190 schools. Data are weighted to be representative of the US population of students in grades 4 and 8, each for the entire nation and every state. Results are reported as average scores on a 0 to 500 scale and as percentages of students performing at or above the NAEP achievement levels: NAEP Basic, NAEP Proficient, and NAEP Advanced. In this study, we focus on changes in the percentages of students at or above the NAEP Basic level, which is the minimum competency level expected for all students across the nation. As a supplement to the NAEP assessment data, this study uses the NAEP School Dashboard (see https://ies.ed.gov/schoolsurvey/mss-dashboard/), which surveyed approximately 3,500 schools each month at grades 4 and 8 each during the pandemic period of January through May 2021: 46 states/jurisdictions participated, and 4,100 of 6,100 sampled schools responded. This study uses state-level information on the percentages of students who received in-person vs. remote/hybrid instructional modes. The school-reported remote learning enrollment rate is highly correlated with the NAEP survey student-reported remote learning experience (during 2021) across grades and subjects (r = .82 for grade 4 reading, r = .81 for grade 4 math, r = .79 for grade 8 reading, r = .83 for grade 8 math). These strong positive correlations provide supporting evidence for the cross-validation of remote learning measures at the state level. For socioemotional wellness and physical health measures, the National Survey of Children’s Health (NSCH) data are used. The 2018/19 surveys involved about 356,052 households screened for age-eligible children, and 59,963 child-level questionnaires were completed. The 2020/21 surveys involved about 199,840 households screened for age-eligible children, and 93,669 child-level questionnaires were completed. Our analysis focuses on school-age children (ages 6-17) in the data. In addition, the NSCH data are also used to assess the quality of protective and nurturing environment for child development across family, school, and neighborhood settings (see Appendix).

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Truth Or Consequences. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Truth Or Consequences, the median household income for the households where the householder is White increased by $2,864(10.25%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $27,942 in 2011 and $30,806 in 2021.
• Black or African American: In Truth Or Consequences, the median household income for Black or African American households was $18,898 in 2011(2022 inflation-adjusted dollars). However there is no reported data for 2021, indicating a lack of information for this specific year.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/truth-or-consequences-nm-median-household-income-by-race-trends.jpeg" alt="Truth Or Consequences, NM median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Truth Or Consequences.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Truth Or Consequences median household income by race. You can refer the same here

In 2023, the birth rate among Hispanic teenagers aged 15 to 19 years was **** per 1,000 women. In comparison, the birth rate among non-Hispanic Asian teens was just *** per 1,000. This statistic shows birth rates among teenagers and young adult women in the U.S. aged 15 to 19 in 1991 to 2023, by race/ethnicity.

IntroductionLyme disease (LD) incidence in the United States is highly regional, with most cases occurring in 16 high-incidence jurisdictions. LD incidence and severity of disease have been found to vary by race. This study describes racial differences in knowledge, attitudes toward vaccination, and risk practices related to LD.MethodsFour web-based surveys were conducted with adults and caregivers of children in high-incidence jurisdictions and 10 states neighboring them. Respondents were recruited via an established online panel to represent the general population. Self-reported race was pooled into 3 categories: ‘White’, ‘Black or African American’, and ‘Other’ for analysis. Analyses were conducted separately for each jurisdiction (high-incidence vs. neighboring) and respondent type (adult vs. caregiver).ResultsThe final sample across all surveys included 2,249 respondents who identified as White, 493 respondents who identified as Black or African American, and 674 respondents of other races. White respondents were older, had higher incomes, and were likelier to live in small cities and rural areas. Though attitudes toward vaccination in general were similar between racial categories, when differences were present, Black respondents were more likely to have concerns about vaccines than White respondents. In all surveys, White respondents engaged in more outdoor activities than Black respondents and performed these activities more often. However, both White adults and caregivers in high-incidence jurisdictions were significantly less likely to have occupations with primarily outdoor work than corresponding respondents in other racial groups. Black respondents also had lower knowledge about LD than White respondents across all surveys. This difference was significant after adjusting for state incidence level and urbanicity.ConclusionThere are some racial differences in knowledge, attitudes, and practices around LD, with White respondents reported having higher knowledge of LD, less concerns about vaccines, and higher frequency of risk practices. These differences might contribute to racial disparities in LD outcomes.

Floods are increasingly frequent and severe due to climate change, thereby impacting migration within the United States. Considering that Black and Brown populations are disproportionately exposed to floods, less likely to receive disaster-related government funds, and vulnerable during subsequent displacement, an examination of differences in migration patterns across racial/ethnic groups is critical. The prevailing conjecture is that after floods, Black and Brown populations will migrate while White ones remain in place. We test this hypothesis by examining the effect of floods on migration across all U.S. county-pairs between 2006-2016 and find that this hypothesis is incorrect: generally, after floods Black populations remain in place and White populations migrate. However, this pattern reverses when the Federal Emergency Management Agency provides financial support. Notably, migration by Hispanic and Asian populations is not significantly affected by floods. These results provide the first evidence of racial disparities in climate migration.