Overlooked by US

Hover over the canvas to experience the data.

see the code

What is going on here

Feminist data principle four, "rethink binaries and hierarchies" inspired me to look into who are not counted in the US government's pandemic response. Since the beginning of March, I have been following the curve that shows the development of confirmed cases, mortality rate and recovery rate across the US. Governors would always refer to these charts during routine public briefings, as well as when introducing the state's plan for the upcoming phase. By and large, the pandemic response in the US has been heavily data-driven. The number of tests, confirmed cases, mortality and recovery rates have been informing and guiding every state's response efforts as well as public health policies. However, this data-driven approach can be a double-edged sword. The data collected can misguide response efforts by not considering affected populations who are not counted- for one, the state-level data is biased against more than 61 million people with disability in America due to the way medical data was collected. COVID public health data in the US is primarily derived from medical record information, such as statistics related to covid testing, mortality rate, and hospitalization rate. This source of data poses two inherent problems: first, disability information is not usually collected in hospitals and clinics. Data on disability is not a classification built into the data collection process. Second, people with vision or cognitive impairment not only face barriers in obtaining public health update on the pandemic, they also face more challenge accessing testing and non-urgent healthcare during this time. This lack of disability-related data has ramifications on how well we as a society can recover from the pandemic, without data and surveys reflecting experiences of people with disabilities, their needs are also missing from the government's public policies and its preparedness, mitigation and response strategies.

Currently, most states require hospitals to submit 3 types of demographics information along with testing and mortality data- age, sex and race/ethnicity. The goal of my translation device is to draw attention to the absence of disaggreted data on disability. The dataset I used came from CDC's website: https://covid.cdc.gov/covid-data-tracker/#demographics

Left: dragging tab on canvas to reveal different layers of disaggregated data.
Right: a person at cursor, we see where he would fall on each pie chart.

Reflection

Working on this project heightened my awareness of where the data came from. The way data was collected provides crucial context for how we make sense of the data. Putting on a feminist lens to look at data pushed me to think critically about what is amiss, besides what is present. Data provides us insights into problems, but it is easy to overlook problems in the data. Those who are not counted will not be heard.

Sources

Article on why we need disaggregated data to guide public messaging: USA Today article

Insights on Covid-19 health equity data reporting in the US: USA Today article