A Spatio-Demographic Perspective on the Role of Social Determinants of Health and Chronic Disease in Determining a Population's Vulnerability to COVID-19

Jessica Embury, BS; Ming-Hsiang Tsou, PhD; Atsushi Nara, PhD; Eyal Oren, PhD


Prev Chronic Dis. 2022;19(6):e38 

In This Article

Abstract and Introduction


Introduction: During the COVID-19 pandemic, health and social inequities placed racial and ethnic minority groups at increased risk of severe illness. Our objective was to investigate this health disparity by analyzing the relationship between potential social determinants of health (SDOH), COVID-19, and chronic disease in the spatial context of San Diego County, California.

Methods: We identified potential SDOH from a Pearson correlation analysis between socioeconomic variables and COVID-19 case rates during 5 pandemic stages, from March 31, 2020, to April 3, 2021. We used ridge regression to model chronic disease hospitalization and death rates by using the selected socioeconomic variables. Through the lens of COVID-19 and chronic disease, we identified vulnerable communities by using spatial methods, including Global Moran I spatial autocorrelation, local bivariate relationship analysis, and geographically weighted regression.

Results: In the Pearson correlation analysis, we identified 26 socioeconomic variables as potential SDOH because of their significance (P ≤ .05) in relation to COVID-19 case rates. Of the analyzed chronic disease rates, ridge regression most accurately modeled rates of diabetes age-adjusted death (R2 = 0.903) and age-adjusted hospitalization for hypertensive disease (hypertension, hypertensive heart disease, hypertensive chronic kidney disease, and hypertensive encephalopathy) (R2 = 0.952). COVID-19 and chronic disease rates exhibited positive spatial autocorrelation (0.304≤I≤0.561, 3.092≤Z≤6.548, 0.001≤P≤ .002), thereby justifying spatial models to highlight communities that are vulnerable to COVID-19.

Conclusion: Novel spatial analysis methods reveal relationships between SDOH, COVID-19, and chronic disease that are intuitive and easily communicated to public health decision makers and practitioners. Observable disparity patterns between urban and rural areas and between affluent and low-income communities establish the need for spatially differentiated COVID-19 response approaches to achieve health equity.


As the novel coronavirus spread throughout the US in early 2020, reports of health disparity challenged claims that COVID-19 was society's "great equalizer".[1,2] As of September 2021, non-Hispanic Black Americans, non-Hispanic American Indians, and Hispanic Americans experienced higher rates of COVID-19 infection (1.1, 1.7, 1.9 times higher, respectively), hospitalization (2.8, 3.5, 2.8 times higher, respectively), and death (2.0, 2.4, 2.3 times higher, respectively) than non-Hispanic White Americans.[3] This observed health disparity stems from widespread structural discrimination and its effects on people of color.

Social determinants of health (SDOH) are socio-environmental conditions that dictate how people live and age, whereas differences in these conditions define socioeconomic status (SES).[4] Low SES is directly linked to poor health outcomes for communicable and noncommunicable diseases alike.[5,6] In a study of COVID-19 outcomes in a New York City hospital, Black and Hispanic patients were more likely than White patients to present with comorbidities, such as cardiovascular disease or diabetes, that were strongly associated with mortality.[7] Dr Anthony Fauci, the immunologist leading the US COVID-19 response, said that the comorbidities that negatively affect COVID-19 outcomes "relate to the social determinants of health dating back to disadvantageous conditions that some people of color find themselves in from birth".[8] Existing research confirms the associations between the disproportionate impact of COVID-19 and chronic disease in socially disadvantaged communities.[6,9,10] The compounding effect of low SES, comorbidities, and COVID-19 demands immediate action to support communities vulnerable to COVID-19.

Our goal was to classify the relationships between COVID-19, chronic disease, and socioeconomic variables to promote localized public health policies. We used a spatially explicit modeling approach to meet our 2 study objectives: 1) to determine which socioeconomic variables, correlated with COVID-19 and chronic disease rates, are potential SDOH, and 2) whether spatial modeling of chronic disease rates can identify communities most vulnerable to COVID-19.