COVID-19 in the United States: Demographics, Economics and Health

We explore the impact that demographic, social and economic factors can have on COVID-19 outcomes across states in the United States (US). We refer to some recent research and also use various statistical methods to assess the data, which provide insights on the relationship between COVID-19 outcomes (confirmed cases and related deaths), demographic variables (population size, race and ethnic minorities’ share in population) and economic factors (income and inequality).

While our analysis is preliminary and based on evolving data related to the pandemic, it is our view that a greater focus on health and income inequality is vital in developing resilience against such health shocks in the future. We also think that more than any single factor, a complex of factors should explain deaths related to this evolving virus.

Coronavirus and the US

Although the US has the highest number of reported cases of COVID-19 (2.3 million) and related deaths (120,000), the pandemic’s impact has varied widely across the US states. For instance, the number of confirmed COVID-19 cases per 100,000 ranges from 1,973 in New York to 51 in Hawaii (Figure 1). Demographics and socio-economic differences are important in explaining these inter-state differences.

Harold Clarke and Paul Whiteley (2020)²  show that population size, population density, economic inequality and states’ health care rank could help predict COVID-19 related deaths in the US. Ewen M. Harrison et al. (2020)³  show that South Asian ethnic minorities in the UK are at a greater risk of dying from COVID-19, partly due to pre-existing diabetic conditions among the populace. Uma V. Mahajan and Margaret Larkins-Pettigrew (2020)⁴  find a weak but very significant positive relationship between the percentage of African Americans and the percentage of COVID-19 confirmed cases, deaths and case mortality in counties within the US.

Gini Coefficient and COVID-19 Cases

Confirmed cases per 100,000 have a strong positive correlation with Gini coefficient (a measure of inequality, Figure 2). A higher Gini coefficient indicates greater inequality with high-income individuals cornering a much larger percentage of the total income of the population. States with higher Gini coefficients that represent greater income inequality, such as DC, NY and CT, tend to have higher numbers of COVID cases and deaths per 100,000 population.

Similarly, there exists a strong link between real personal income and confirmed COVID-19 cases and deaths. States with high real personal income are more likely to experience a higher number of COVID-19 cases per 100,000 than others (Figure 3). This is because richer states are more urbanized, have higher population density and are more unequal in terms of income distribution. As such, states with denser populations are more likely to see higher numbers of COVID-19 cases and deaths than others (Figure 4).

Figure 5 presents the variables that positively correlate with COVID-19 cases and deaths. Based on the Spearman rank correlation coefficient analysis (to understand the strength and direction of a relationship between two variables), we note that there is a strong positive relationship between each of the variables (real personal income, population density, income inequality as well as race and ethnic minority population) and confirmed COVID-19 cases.

In the context of fighting such global epidemics, we concur with the conclusions of some ongoing studies, which state that there should be a greater emphasis on income and health equality. This should allow for greater resilience to health shocks such as COVID-19 or SARS even in richer states such as DC, NY or CT.