Investigating the Impact of COVID-19 on Different Regions in Detroit
By Zahida Malik
The COVID-19 pandemic highlights the racial and economic divide in the United States, as well as throughout the world. One stark example can be found in the City of Detroit. Among the poorest and unhealthiest big cities in the USA [1,2], it became an epicenter during the early weeks of the pandemic outbreak in the United States. The COVID-19 infection data suggested that people of color are disproportionately impacted by the pandemic across the nation and this trend is clear in the City of Detroit.
Because of the early hot spots in Detroit, the data analytics team at Authority Health studied the relationship between COVID-19 hot spots in the City of Detroit and different factors that impact health to explore if any distinguished hot spots from non-hot spots. One factor that we studied was occupation data. Using the Bureau of Labor Statistics and O*Net data, a study published on April 15, 2020, evaluated and ranked the risk of contracting Covid-19 in human service occupations based on 3 factors: 1. Contact with others; 2. Physical proximity; and 3. Exposure to disease and infection (Figure 1) . Most of the highest risk occupations were related to health care, front line workers across occupation types, and teachers or educators.
Figure 1. Visual representation of risk of Covid-19 by human service occupation type.
Using this knowledge, we set out to determine if the City of Detroit hot spots had significantly higher proportions of high-risk occupations than non-hot spots. In addition to the human service fields identified in the study above, we hypothesized production and manufacturing type occupations would also carry a higher risk of COVID-19, as factories have thousands of workers.
In order to determine if occupation type distinguished a hot spot from a non-hot spot in Detroit, we divided zip codes into hot spot or non-hot spot based on their Covid-19 case rates. We ran correlations on Census Bureau data by occupation type in hot spots and non-hot spots and used a t-test to determine if there was a significant difference between them.
After determining the number of people employed in each zip code by occupation type using the Census data, we plotted each against the number of Covid-19 cases per 100,000. Figure 2 demonstrates a scatter plot showing the correlation.
Figure 1: Scatter plot showing correlation of number employed in the education occupation category versus the number of COVID-19 cases per 100,000.
A moderate positive correlation is seen with r = 0.4949.
Further analysis was performed using a T-test to compare average numbers in each occupation type in hot spots and non-hot spots to see if any distinguished hot spots. Our analysis revealed that three occupation types were statistically significantly higher in zip codes identified as hot spots versus non-hot spots: health care, education, and social services (p = 0.018, 0.003, and 0.014). There was no difference in the number of people employed in production and manufacturing between the groups (p = 0.392).
Our study found there are occupations significantly associated with a higher risk for COVID-19 infection. Examining hot spot regions in Detroit using occupation data, our findings corroborate with the results of other studies on occupation risk during the pandemic. This is one clue to solving the mystery of why hot spots occur.
 Detroit Free Press, “Detroit is the unhealthiest city in America” https://www.freep.com/story/news/local/michigan/detroit/2017/02/13/detroit-healthiest-cities-america/97849272/.
 Detroit News, “Detroiters’ income rises for the second year but poverty rate doesn’t improve”, https://www.detroitnews.com/story/news/local/detroit-city/2018/09/13/census-detroiters-income-rise/1268641002/.
 Lu, Marcus. “The Front Line: Visualizing the Occupations with the Highest COVID-19 Risk.” Visual Capitalist, 15 Apr. 2020, www.visualcapitalist.com/the-front-line-visualizing-the-occupations-with-the-highest-covid-19-risk/.
Zahida Malik is a Masters of Health Information Technology student at the University of Michigan – Dearborn.