A Spatio-Demographic Perspective on the Role of Social Determinants of Health and Chronic Disease in Determining a Population’s Vulnerability to COVID-19
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields



Publication Date Range:


Document Data


Document Type:






Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page


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

Filetype[PDF-860.15 KB]

  • English

  • Details:

    • Alternative Title:
      Prev Chronic Dis
    • Description:

      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.


      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.


      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.


      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.

    • Pubmed ID:
    • Pubmed Central ID:
    • Document Type:
    • Main Document Checksum:
    • File Type:

    You May Also Like

    Checkout today's featured content at stacks.cdc.gov