Can Socioeconomic Status Predict COVID-19 Infection Rates?

Santa Clara County (SCC) is one of California's largest counties, with a population of over 1.7 million residents as of the 2010 Census. [1] It is also home to the city of San Jose, the heart of Silicon Valley and the most populous city in the San Francisco Bay Area. Santa Clara County is a rapidly growing county for technology as high tech companies seek to establish their bases among giants in the area such as Apple, Samsung, Google, Adobe, and Intel. Although it is home to lots of potential and diversity, Santa Clara County in its rapid growth also risks experiencing widening amounts of socioeconomic disparity which may in turn translate to health disparity. Studying the socioeconomic and health disparities of Santa Clara County may reveal valuable information about the reality of residing in an ever increasingly expensive area.

It is well established that socioeconomic factors such as neighborhood disadvantage, education levels, and race/ethnicity are factors that contribute to community health. [2-4] One study analyzing spatial patterns in COVID-19 inequity in major cities found that measures such as positivity rate, case rate, and mortality rate were geographically clustered and linked to social vulnerability. [5] Understanding if COVID-19 incidence is geographically clustered and if the pandemic disproportionately affects specific marginalized neighborhoods may help policymakers, government bodies, and organizers pass legislation and implement polices and programs to reduce socioeconomic and health inequities exacerbated by the COVID-19 pandemic.

In this project I employed statistical and geographical methodologies to study the spatial distribution of COVID-19 as well as the connection between wealth and socioeconomic status (SES) as a potential indicator of adverse COVID-19 incidence rates in Santa Clara County.

• Is COVID-19 spatially autocorrelated across neighborhoods in Santa Clara County?
• How much disparity exists for income and COVID-19 incidence rates? Is socioeconomic status, measured as median household income, correlated to COVID-19 incidence rates across neighborhoods in Santa Clara County? How so?

For my study of COVID-19 spatial distribution and its relation to socioeconomic status, I conducted my research in two phases.

In the first phase of the study, I wanted to determine if COVID-19 incidence in SCC was geographically clustered (spatially autocorrelated). I examined the existence of spatial autocorrelation of COVID-19 in SCC by mapping COVID-19 incidence rates (case rates) across various neighborhoods in SCC, using ZIP Code Tabulated Areas (ZCTAs) as my unit of observation. I also visualized the spatial autocorrelation of COVID-19 incidence by creating a Moran's scatterplot to illustrate the connection between a neighborhood's incidence rate to its neighbors' average incidence rate. I then quantified spatial autocorrelation by calculating the Moran's I index value for incidence rate using queen adjacency and standardized weights.

In the second phase of the study, I analyzed the disparities in socioeconomic status (using median household income as a proxy) and COVID-19 incidence as well as the correlation coefficient between SES and COVID-19 incidence. For this phase, I calculated numeric indicators of disparity including the 90/10 ratios and interquartile ranges (IQRs) for median household income and COVID-19 incidence rate. I also mapped the spatial distribution of COVID-19 incidence rate and compared it to the map of median household income.

All COVID-19 data in this study was sourced from the County of Santa Clara Open Data Portal (cumulative from 2020-2021) [6] and 2019 American Community Survey (ACS) Census (5-year estimate). [7]

All data analysis, calculations, and mapping accomplished in R via RStudio and ArcGIS.

Considering the map of incidence distribution, the Moran's scatterplot, and the Moran's I value, we can conclude that there is strong positive spatial autocorrelation (geographic clustering) of COVID-19 incidence in Santa Clara County.

In order to more clearly compare the stark differences in the spatial distributions of SES and COVID-19 incidence, here are the two maps of SCC we've already seen in static form.

Notice any connections between how the darker clusters of one map translate to the lighter clusters of the other map? Seeing the maps of COVID-19 incidence and median neighborhood income side by side, it's clear that the two variables are inversely correlated.

The 90/10 and IQR ratios indicate existing disparities between neighborhoods in terms of socioeconomic status and COVID-19 incidence. Moreover, considering the correlation coefficient, scatterplot of income to incidence, and maps of income and incidence, we can conclude that socioeconomic status (as median household income) is strongly negatively correlated to COVID-19 incidence rate in Santa Clara County.

Based on the results of this research, I have established that not only is COVID-19 geographically clustered within Santa Clara County among already-existing disparity, but that socioeconomic status as measured by median household income is strongly negatively correlated with incidence rate. In other words, socioeconomic status is a strong predictor of a neighborhood's COVID-19 burden, adding onto growing evidence that socioeconomic factors are an important determinant of health. From differences in occupational status to differential access to healthcare, the COVID-19 pandemic has contributed to a long and ever increasing list of health burdens disproportionately experienced by marginalized communities.

In our mission to further the health of communities and ensure that everyone has equitable health outcomes, we need to remember that health outcomes are not limited to individual responsibility or healthcare alone.

We need to work together to ensure that socioeconomic and health disparities as well as the systems that continue to perpetuate these disparities are eliminated through collective effort and fundamental change.

1. Bureau UC. Decennial Census by Decades. The United States Census Bureau. Accessed March 16, 2021. https://www.census.gov/programs-surveys/decennial-census/decade.html
2. Quan D, Luna Wong L, Shallal A, et al. Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19. J Gen Intern Med. Published online January 27, 2021. doi:10.1007/s11606-020-06527-1
3. Hawkins RB, Charles EJ, Mehaffey JH. Socio-economic status and COVID-19–related cases and fatalities. Public Health. 2020;189:129-134. doi:10.1016/j.puhe.2020.09.016
4. Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health. 2020;74(11):964-968. doi:10.1136/jech-2020-214401
5. Bilal U, Tabb LP, Barber S, Roux AVD. Spatial Inequities in COVID-19 Testing, Positivity, Confirmed Cases and Mortality in 3 US Cities: an Ecological Study. medRxiv. Published online February 1, 2021:2020.05.01.20087833. doi:10.1101/2020.05.01.20087833
6. COVID-19 cases by zip code of residence | County of Santa Clara. County of Santa Clara Open Data Portal. Published March 14, 2021. Accessed March 15, 2021. https://data.sccgov.org/COVID-19/COVID-19-cases-by-zip-code-of-residence/j2gj-bg6c
7. Bureau UC. Data Profiles. The United States Census Bureau. Published 2019. Accessed March 16, 2021. https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2019/