King County Health Disparities Dashboard

Racial/ethnic health disparities are higher rates of serious health conditions or deaths that affect communities of color. These disparities can result in shorter lifespans and lower quality of life, are rooted in inequities in the opportunities and resources needed for good health, such as education, employment, safe and healthy neighborhoods, and access to health care. These inequities are often the result of current and historical institutionalized racism or explicit racial bias.

Click and hover over the interactive dashboard to learn more.

What is in the Dashboard?
The King County Health Disparities Dashboard documents how much specific disparities affect communities of color in King County. It shows health-related rates for the following groups compared to whites (or the county average, if you prefer): American Indian/Alaska Native, Asian, Black/African American, Hispanic, and Native Hawaiian/Pacific Islander. The dashboard includes:

  • Color-coded dots showing the relationship of rates in communities of color relative to the chosen comparison group (White residents or the King County average).

  • The dots show the magnitude of disparity as a rate ratio—the ratio of the community-of-color rate to the rate in whites or the King County average.

  • Bar charts show the rates of indicators in King County communities of color with lines representing the rates for the King County average (solid) and white residents (dashed).

Selected Findings
This dashboard includes a wealth of information. Below are examples that show how the dashboard can be used to explore disparities.

  • Hispanics face several challenges accessing healthcare in King County. Compared to whites, they have rates 2.6 times higher for not seeing a doctor due to costs; 5.4 times higher for not having health insurance; and 1.7 times higher for having late or no prenatal care. This translates to about 6,900 people every year who don’t see a doctor because of the cost.

  • American Indian/Alaska Natives experienced large disparities in infant mortality, with a rate 4.1 times higher than whites.

  • Black/African American King County residents have a homicide mortality rate 10.7 times higher than white King County residents. This difference is even greater - 14.4-fold – among men. An average of 21 Black people die by homicide in King County each year.

  • Although it remains rare in King County, some of the largest disparities are in tuberculosis. Asians have a tuberculosis rate about 34 times higher than whites. American Indian/Alaska Natives, Black, Hispanics, and Native Hawaiian/Pacific Islanders all have rates 8 to 32 times higher whites.

While these data reflect many disparities, not all racial and ethnic groups fare worse than whites, or than the King County average, on all indicators. For example:

  • Asians fare better than whites on many health indicators. They have lower death rates by cancer, Alzheimer’s, accidents, and heart disease; lower teen birth rates; and lower rates of asthma, smoking, and obesity. (However, “Asians” are a group with wide linguistic, social, and cultural differences, and without further disaggregation among subgroups - such as Vietnamese, Korean, and South Asians – the potential disparities within these groups remain hidden.)

  • Consistent with other evidence of the Hispanic mortality paradox, Hispanics have lower rates of many causes of death, including drug-related death; cancer; suicide; heart disease; and more.

  • Black/African American residents of King County have only half the rate of suicide deaths that whites do.

A key limitation of the dashboard is the race and ethnicity groupings for which data is shown. Each category includes diverse communities, with differing languages, national origins, and more, and grouping them together can mask disparities within those groups. For clarity, we’ve chosen to limit disaggregation to race/ethnicity groups that are consistently available across the datasets included in the dashboard.

We invite users to contact us at about disaggregation in any specific indicator, clarification on how to interpret the data or with any other questions.