COVID-19: Racial and Ethnic Disparities

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Unequal and unjust impact of COVID-19

This pandemic reinforces the fact that every person's health is intertwined with the health of others in their community. However, the racial and ethnic disparities observed in these data demonstrate that Black, Brown, Indigenous, and other communities of color are suffering a disproportionate impact, and bearing the brunt of the COVID-19 pandemic.

In our society, race and ethnicity are often used to group people according to shared characteristics or identities.

  • Race refers to a person's physical traits such as facial features, hair, and skin color.
  • Ethnicity refers to a person's cultural identity and regional ancestry.

Each person who tested positive was asked to identify with a race category and with or without a Hispanic ethnicity. It is important to note that qualities inherent to individuals of any given racial or ethnic background are not causing the disparities, rather, the differences in opportunity and resources between racial and ethnic groups have caused this unequal impact of COVID-19 in communities in Wisconsin. 

Inequities caused by racism and other forms of discrimination are well documented and have intensified during the pandemic. A better understanding of how racism – systemic and otherwise – has exacerbated the disparate impacts of COVID-19 helps us to better serve these communities. Public health has long known that a person’s social, economic, and physical environment shape their health more than any other factor, and these factors are directly tied to racism.

People who work in essential jobs or live in high-density housing are more likely to contract the virus because of unavoidable person-to-person interaction, and those who have less access to quality health care and certain chronic diseases have more severe outcomes. These conditions for greater exposure and worse outcomes have been concentrated in communities of color due to decades of deliberate policy choices and racist institutional practices like systemic housing discrimination of Black families, disinvestment from low-income neighborhoods, and breaking treaties with Tribal nations. 

For example, racial residential segregation has led to poorly-funded schools and educational outcomes for these communities, resulting in employment opportunities that are limited to sectors with high-exposure, low-paying frontline jobs that provide little protections for workers’ rights, often do not come with benefits, and fail to provide adequate paid sick leave. Furthermore, disinvestment from these neighborhoods has left communities with fewer grocery stores, lacking safe places to exercise, and more polluted air and water resulting in higher rates of chronic diseases, which can lead to more severe outcomes from COVID-19. These long-standing unjust conditions become compounded by an unequal response when, for example, those in frontline jobs, as well as the communities at large, are under-resourced with personal protective equipment, have limited access to testing, and lack the necessary investment in community infrastructure and public health to appropriately respond to a crisis of this magnitude. 

These social factors, among others, vary by race and ethnicity because they have been and continue to be shaped by racism and discrimination, which create unfair vulnerabilities and barriers for these populations. Despite these barriers, many tribes and communities of color have limited the devastating impacts of COVID-19 in their communities by working together as a community and taking public health precautions seriously, such as limiting their activity out in their communities, wearing face coverings, and physical distancing.

The case, hospitalization, and death data may not reflect the full extent of the disproportionate burden COVID-19 has had in some communities because these data depend on factors like access to testing and hospital care, which are disproportionately limited among communities of color, due to things like unequal access to transportation or health insurance. These data represent the progression of the outbreak to-date and patterns of infection may change as the pandemic continues. 

Learn more about racial inequities and what we can do.

Understanding our data: What does this chart mean?

These charts show us the health impact of COVID-19 by race and ethnicity. The first chart shows the percent of all COVID-19 cases and the rate (cases per 100,000 people) for each racial and ethnic group. It also shows the percent of the general population each racial and ethnic group makes up in Wisconsin.

The second chart shows us the weekly rate as a trend over time for each racial and ethnic group. Using the filter on the left, we are able to look at these trends over time for cases, hospitalizations, or deaths.

Please note that the Wisconsin Electronic Disease Surveillance (WEDSS) system underwent routine maintenance and enhancements over the weekend of October 16-18, 2020. Due to this temporary pause in reporting, multiple days of data were uploaded at once, affecting the single day count for the visualizations during that time.

About our data: How do we measure this?

Individuals in the Hispanic and Latino group could be of any race, and those in the other racial/ethnic groups are not Hispanic or Latino.

Rates are a ratio of the number of cases (or hospitalizations, deaths) to the whole population for each racial/ethnic group within a given time-period. Because rates are a ratio that adjusts for the size of the population, rates allow us to make an apples-to-apples comparison of the impact of COVID-19 on communities of different sizes. Circles of equal diameter or lines of equal height would indicate equal impact across groups. A time-period of weekly is used in the circle chart to show how the impact of the epidemic has changed over time, and a time-period of the entire duration of the pandemic is used for the circle chart to show the cumulative burden for each group. These distributions are changing and represent the progression of the outbreak to date. 

When the date of symptom onset, hospital admission, or death were unknown, the date was approximated based on the day the record was created. Weekly rates are subject to change as more information about individuals with positive tests becomes available. Rates in the most recent 1-2 weeks may be artificially low because of time lags between date of symptom onset and testing and between testing and reporting positive cases.

Data source: Wisconsin Electronic Disease Surveillance System (WEDSS). Population estimates are from the U.S. Census Bureau annual estimates of the resident population for Wisconsin: July 1, 2019 (SC-EST2019-SR11H-55).

Read our Frequently Asked Questions for more information on how cases of COVID-19 are reported to WEDSS.

Every morning by 9 a.m., we extract the data from WEDSS that will be reported on the DHS website at 2 p.m. These numbers are the official DHS numbers. Counties may report their own case and death counts on their own websites. Because WEDSS is a live system that constantly accepts data, case and death counts on county websites will differ from the DHS counts if the county extracted data from WEDSS at a different time of day. Please consult the county websites to determine what time of day they pull data from WEDSS. Combining the DHS and local totals will result in inaccurate totals.

Confirmed cases of COVID-19: Unless otherwise specified, the data described here are confirmed cases of COVID-19 reported to WEDSS. Cases are classified using the national case definition established by the CDC. Confirmed cases are those that have positive results from diagnostic, confirmatory polymerase chain reaction (PCR) tests or nucleic acid amplification tests (NAT) that detect genetic material of SARS-CoV-2, the virus that causes COVID-19. Illnesses with only positive antigen or positive antibody test results do not meet the definition of confirmed and are not included in the number of confirmed cases.

COVID-19 Deaths: Unless otherwise specified, COVID-19 deaths reported on the DHS website are deaths among confirmed cases of COVID-19 that meet the vital records criteria set forth by the CDC and Council of State and Territorial Epidemiologists (CSTE) case definition. Those are deaths that have a death certificate that lists COVID-19 disease or SARS-CoV-2 as an underlying cause of death or a significant condition contributing to death. Deaths associated with COVID-19 must be reported by health care providers or medical examiners/coroners, and recorded in WEDSS by local health departments in order to be counted as a COVID-19 death. Deaths among people with COVID-19 that were the result of non-COVID reasons (e.g., accident, overdose, etc.) are not included as a COVID-19 death. For more information see the FAQ page.

Probable cases of COVID-19 and deaths among probable cases. Some visualizations include the option of including information on probable cases of COVID-19 and deaths among probable cases of COVID-19. Cases are classified using the national case definition established by the CDC and the CSTE [https://wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/.... A person is counted as a probable* case of COVID-19 if they are not positive by a confirmatory laboratory test method (for example, a PCR, or NAT test), but have met one of the following:

  1. Test positive using an antigen test method.
  2. Have symptoms of COVID-19 AND known exposure to COVID-19 (for example, being a close contact of someone who was diagnosed with COVID-19).
  3. COVID-19 or SARS-CoV-2 is listed on the death certificate.

*This definition was updated as of August 19, 2020. Previously, probable cases also included those that had a positive antibody test which detects COVID-19 antibodies in the blood. For more details on this transition, see the CDC’s statement.

Deaths among probable cases are those that meet one of the following criteria:

  • A probable case of COVID-19 is reported to have died from causes related to COVID-19.
  • A death certificate that lists COVID-19 disease or SARS-CoV-2 as an underlying cause of death or a significant condition contributing to death is reported to DHS but WEDSS has no record of confirmatory laboratory evidence for SARS-CoV-2.

People with negative test results: The number of people with negative test results includes only Wisconsin residents who had negative confirmatory test results (PCR or NAT tests that detect pieces of SARS-CoV-2) reported electronically to WEDSS or entered manually into the WEDSS electronic laboratory module. Because manual entry of negative test results into electronic laboratory module takes more time, this number underestimates the total number of Wisconsin residents with negative test results.

Data shown are subject to change. For more information see the FAQ page. As individual cases are investigated by public health, there may be corrections to the status and details of cases that result in changes to this information. Some examples of corrections or updates that may result in the case or death counts going up or down, include:

  • Update or correction of case’s address, resulting in a change to their location of residence to another county or state
  • Correction to laboratory result
  • Correction to a case’s status from confirmed to unconfirmed (for example, if they were marked as confirmed because a blood test detecting antibodies was positive instead of a test detecting the virus causing COVID-19)
  • De-duplication or merging and consolidation of case records
  • Update of case’s demographic information from missing or unknown to complete information

For information on testing, see: COVID-19, testing criteria section.

We plan to update our data daily by 2 p.m.

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How can I download DHS COVID-19 data?

All DHS COVID-19 data is available for download directly from the chart on the page. You can click on the chart and then click "Download" at the bottom of the chart (gray bar).

To download our data visit one of the following links:

You can find more instructions on how to download COVID-19 data or access archived spatial data by visiting our FAQ page. The data dictionary(PDF) provides more information about the different elements available in the data above.

Last Revised: November 18, 2020