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TwitterUpdated weekly on Mondays The dashboard below shows the impacts of COVID-19 on communities of color compared to whites in King County, Washington.
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The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)
| Key | List of... | Comment | Example Value |
|---|---|---|---|
| County | String | County name | "Abbeville County" |
| State | String | State name | "SC" |
| Age.Percent 65 and Older | Float | Estimated percentage of population whose ages are equal or greater than 65 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico). | 22.4 |
| Age.Percent Under 18 Years | Float | Estimated percentage of population whose ages are under 18 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico). | 19.8 |
| Age.Percent Under 5 Years | Float | Estimated percentage of population whose ages are under 5 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico). | 4.7 |
| Education.Bachelor's Degree or Higher | Float | Percentage for the people who attended college but did not receive a degree and people who received an associate's bachelor's master's or professional or doctorate degree. These data include only persons 25 years old and over. The percentages are obtained by dividing the counts of graduates by the total number of persons 25 years old and over. Tha data is collected from 2015 to 2019. | 15.6 |
| Education.High School or Higher | Float | Percentage of people whose highest degree was a high school diploma or its equivalent people who attended college but did not receive a degree and people who received an associate's bachelor's master's or professional or doctorate degree. These data include only persons 25 years old and over. The percentages are obtained by dividing the counts of graduates by the total number of persons 25 years old and over. Tha data is collected from 2015 to 2019 | 81.7 |
| Employment.Nonemployer Establishments | Integer | An establishment is a single physical location at which business is conducted or where services or industrial operations are performed. It is not necessarily identical with a company or enterprise which may consist of one establishment or more. The data was collected from 2018. | 1416 |
| Ethnicities.American Indian and Alaska Native Alone | Float | Estimated percentage of population having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment. This category includes people who indicate their race as "American Indian or Alaska Native" or report entries such as Navajo Blackfeet Inupiat Yup'ik or Central American Indian groups or South American Indian groups. | 0.3 |
| Ethnicities.Asian Alone | Float | Estimated percentage of population having origins in any of the original peoples of the Far East Southeast Asia or the Indian subcontinent including for example Cambodia China India Japan Korea Malaysia Pakistan the Philippine Islands Thailand and Vietnam. This includes people who reported detailed Asian responses such as: "Asian Indian " "Chinese " "Filipino " "Korean " "Japanese " "Vietnamese " and "Other Asian" or provide other detailed Asian responses. | 0.4 |
| Ethnicities.Black Alone | Float | Estimated percentage of population having origins in any of the Black racial groups of Africa. It includes people who indicate their race as "Black or African American " or report entries such as African American Kenyan Nigerian or Haitian. | 27.6 |
| Ethnicities.Hispanic or Latino | Float |
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Twitter*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***
The dataset provides information about the demographics and characteristics of COVID-19 cases by racial/ethnic groups among Santa Clara County residents. Source: California Reportable Disease Information Exchange. Data notes: The Other category for the race/ethnicity graph includes American Indian/Alaska Native and people who identify as multi-racial.
This table is updated every Thursday.
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TwitterExample of assigning ethnic class using Ethnicity Estimator.
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TwitterAs of June 14, 2023, around 24 percent of COVID-19 cases in the U.S. were among people of Hispanic or Latino origin, and 12 percent of cases were among non-Hispanic Blacks. Hispanics or Latinos account for around 18 percent of the U.S. population while non-Hispanic Blacks make up 12.5 percent. This statistic shows the distribution of coronavirus (COVID-19) cases in the United States as of June 14, 2023, by race/ethnicity.
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TwitterNote: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of positive COVID-19 cases among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown. Description The MD COVID-19 - Cases by Race and Ethnicity Distribution data layer is a collection of positive COVID-19 test results that have been reported each day via CRISP. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
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TwitterIn 2023, there were around 1.5 new cases of tuberculosis per 100,000 population among the Hispanic population of the United States. This statistic shows the number of new cases of tuberculosis per 100,000 population in the United States in 2023, by race/ethnicity.
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TwitterThis layer shows Race and Ethnicity. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of population that are Hispanic or Latino (of any race). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B02001, B03001, DP05Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.
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TwitterCOVID-19 Cases and Deaths by Race
Columns:
State Data Source Total positive cases in state Total deaths in state Percentage of Black people represented in total cases Percentage of Black people represented in total deaths Percentage of total population that identify as Black (census) Updated Notes
Data shared under an open data policy at Data for Black Lives (d4bl.org)
Banner Photo by Vince Fleming on Unsplash
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License information was derived automatically
This data set is no longer being updated and is historical, last update 10/10/2022.Provides the percentage of COVID-19 cases by race/ethnicity in Jefferson County, KY. In addition, percentage of Jefferson county vaccine recipients broken out by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. Fieldname Definition race description of race/ethnicity CensusCountPCT percentage of population make-up of Jefferson county ConfirmedCaseCountPCT percentage of confirmed cases by race/ethnicity (rounded to the whole percent) DeceasedCountPCT percentage of deceased cases by race/ethnicity (rounded to the whole percent) RecoveredCountPCT percentage of recovered cases by race/ethnicity (rounded to the whole percent) VaccinatedCountPCT percentage of Jefferson county vaccine recipients by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. (rounded to the whole percent) Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to change
For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.
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TwitterNote: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.
This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).
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TwitterIn 2019-2021, the rate of new cases of diagnosed diabetes among non-Hispanic white adults in the United States was around five per 1,000 population. This statistic shows the rate of new cases of diabetes among U.S. adults in 2019-2021, by race/ethnicity.
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TwitterTable from the American Community Survey (ACS) 5-year series on race and ethnicity related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B03002 Hispanic or Latino Origin by Race, B02008-B02013 Race Alone or in Combination with One or More. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B03002, B02008, B02009, B02010, B02011, B02012, B02013Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.
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TwitterBetween the years 2012 and 2016, there were 372 invasive melanoma cases recorded in the Black population in the U.S. versus 1,725 such cases for Hispanics. The statistic illustrates the number of invasive melanoma cases in the U.S. between 2012 and 2016, by race/ethnicity.
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.
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TwitterThis report summarizes data on COVID-19 cases and COVID-19 associated deaths by race/ethnicity for the state of Connecticut and the 10 largest Connecticut towns. Data on race/ethnicity are missing on almost half (47%) of reported COVID-19 cases. CT DPH has urged healthcare providers and laboratories to complete information on race/ethnicity for all COVID-19 cases. All data in this report are preliminary; data will be updated as new COVID-19 case reports are received and data errors are corrected. Data on COVID-19 cases and COVID-19-associated deaths were last updated on April 20, 2020 at 3 PM. Information about race and ethnicity are collected on the Connecticut Department of Public Health (DPH) COVID-19 case report form, which is completed by healthcare providers for laboratory-confirmed COVID-19 cases. Information about the race/ethnicity of COVID-19-associated deaths also are collected by the Connecticut Office of the Chief Medical Examiner and shared with DPH. Race/ethnicity categories used in this report are mutually exclusive. People answering ‘yes’ to more than one race category are counted as ‘other’.
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TwitterCOVID-19 data for San Francisco from data.sfgov.org
COVID-19 Hospitalizations
COVID-19 Cases Summarized by Date, Transmission and Case Disposition
COVID-19 Cases Summarized by Race and Ethnicity
COVID-19 Cases Summarized by Age Group and Gender
COVID-19 Tests
Data from https://data.sfgov.org/analytics and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Age-Group-and-Gender/sunc-2t3k and https://data.sfgov.org/COVID-19/COVID-19-Tests/nfpa-mg4g and https://data.sfgov.org/COVID-19/COVID-19-Hospitalizations/nxjg-bhem and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Date-Transmission-and/tvq9-ec9w and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Race-and-Ethnicity/vqqm-nsqg
Dataset license: https://datasf.org/opendata/terms-of-use/
Banner Photo by Maarten van den Heuvel on Unsplash
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TwitterThese 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 examined differences in youth's mental health and substance abuse needs in seven different racial/ethnic groups of justice-involved youth. Using de-identified data from the Survey of Youth in Residential Placement (SYRP), it was assessed whether differences in mental health and substance abuse needs and services existed in a racially/ethnically diverse sample of youth in custody. Data came from a nationally representative sample of 7,073 youth in residential placements across 36 states, representing five program types. An examination of the extent to which there were racial/ethnic disparities in the delivery of services in relation to need was also conducted. This examination included assessing the differences in substance-related problems, availability of substance services, and receipt of substance-specific counseling. One SAS data file (syrp2017.sas7bdat) is included as part of this collection and has 138 variables for 7073 cases, with demographic variables on youth age, sex, race and ethnicity. Also included as part of the data collection are two SAS Program (syntax) files for use in secondary analysis of youth mental health and substance use.
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TwitterThis data set is no longer being updated and is historical, last update 10/10/2022. Provides the percentage of COVID-19 cases by ethnicity for the last 8 weeks in Jefferson County, KY.Fieldname Description ethnicity description of ethnicity population_percent proportion of population in identified by ethnicity to total population by_ethinicity Number of confirmed cases identified by ethnicity total_confirmed number of all confirmed cases to date ethinicity_percent Proportion of confirmed cases identified by ethnicity to total number of confirmed cases to date by_ ethinicity_deceased Number of deceased cased identified by ethnicity total_deceased number of all deceased cases to date deceased_percent Proportion of deceased cases identified by ethnicity to total number of deceased cases to date REPORT_BEGIN_DATE The date calculated as 8 weeks before the report end date. The beginning date of the date range of data aggregated. REPORT_END_DATE The end date of the reporting period. Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to changeFor questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.
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TwitterUpdated weekly on Mondays The dashboard below shows the impacts of COVID-19 on communities of color compared to whites in King County, Washington.