10 States: 5 with Race/Ethnicity Data

In Tuesday's post I spent a bit of time reporting the race ethnicity data that was available. I mentioned that because of what the early data was reporting, there would be a call for more states to disaggregate the data by race/ethnicity. Sure enough, today there are five states reporting the start of some race/ethnicity data. Since this was not a focus for the last several weeks, it was mentioned that some of that data will most likely never be reported, especially if the patient was not admitted to a hospital. We will make due with what we have.

As a reminder, the focus of this blog is to look beyond the daily reported national and state level totals and analyze the trends within and between states. For context, the global total as of this post exceeds 1,600,000 and the US has over 465,000. As a note, New York state (161,000) has more than the next closest nation (Spain at 153,000). There is a lot that can be said there, but I will let those numbers speak for themselves.

I did not post yesterday, but there was big news in Maryland with yesterday's data release. You can see on Figure 1, the Maryland trend line took a dramatic leap yesterday. It was reported that a number of previously unreported cases were added to the numbers. In other words, these were positives from around two weeks ago. With the addition of those backlogged tests, Maryland and Colorado are nearly identical, with totals just under Indiana's. Another note, Florida's data continues to be erratic. At this point Pennsylvania has exceeded Florida and will surpass California in the next day or so. Iowa, Arizona, and Missouri are thankfully holding steady.

Figure 1. Total COVID Cases over Time in Ten States

The second chart (see Figure 2) reorganizes the x-axis to show cases from a common starting point, once a state reported its 40th case. There is not much new information to examine except that Colorado held steady at the point where California had spiked. Figure 3 demonstrates the percent increase in cases compared to the previous day. Although the number of cases will continue to rise, as long at the daily percent increase is decreasing, we are on the right track. Outside of Maryland's increase yesterday, the general trend for all ten states is downward. 

Figure 2. Total COVID Cases over Time after 40 Reported Cases in Ten States

Figure 3. Daily Percentage Increase in COVID Cases for Ten States. 

Finally, as I mentioned at the start, five states are now reporting race/ethnicity data. The first state for today will be California. These next three images are all California data. The center graph is the census data. If COVID was even distributed across all race/ethnicity, then the top graph (total cases) and the bottom graph (COVID deaths) should look identical to the center graph. Overall, California seems to be reflective of the census data. However, the state has only reported 54% of the confirmed cases as of today.

Figure 4. Pie Charts for California COVID Cases, Census Data, and COVID Deaths

Michigan and Florida are two states with disproportionate distributions of cases or deaths. Since Michigan numbers were reported last time, we will look at Florida today (see Figure 5). Although the Census graph and the death graph look similar, the cases by race/ethnicity is visually different. The large 33% section in the top graph represents unknown cases. However, we can see that Hispanic/Latino Floridians have a high proportion of cases compared to the percentage of the population they represent, yet are dying at lower rates. However, White Floridians represent a smaller proportion of their percentage of the population, yet are dying at higher rates.

Figure 5. Pie Charts for Florida COVID Cases, Census Data, and COVID Deaths 

There will be much discussion in the following weeks, months, and years regarding the racial/ethnic disparities of this disease.  One piece of data we will never have is socio-economic status (SES). Although hospitals collect basic demographic data on patients as instructed by the CDC, they do not collect SES data. It is too early to tell, but due to significant systemic factors, lower SES folks are most likely going to be impacted significantly. This includes both the rural poor and the urban poor, as they make up the essential workers staffing the food industry and the essential businesses (Target, Walmart, etc.). So for their sake, remember to Stay Home, Stay Safe, and Stay Healthy. JRB