Risk factors for severe COVID-19

• Known risk factors:
  • Age
  • Race/ethnicity
  • Underlying medical condition (e.g., asthma, cancer, etc.)
  • Physical inactivity
  • Smoking
     
• Spatial and contextual factors may be important determinants:
  • Air pollution
  • Green space
  • Neighborhood SES
     
• Gaps:
  • Most studies focused on individual exposures / exposures from a single class
  • Most existing studies on spatial and contextual factors were ecological and based on aggregated COVID-19 outcomes

Study aim

• To conduct a spatial and contextual exposome-wide association study (ExWAS) of COVID-19 hospitalization
   
  • ​​Individual-level electronic health records data of COVID-19 patients
     
  • Linked data from multiple sources to characterize patients’ long-term exposures to the spatial and contextual exposome based on their geocoded residential histories

• OneFlorida+ CRN:
  • 14 academic institutions and health systems
  • ~16 million patients
     
• We obtained data from 50,368 patients with:
  • ≥18 years old
  • A SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis in 03/2020-10/2021
  • ≥1 inpatient encounter or 2 outpatient encounters (≥3 months apart) within 5 years before COVID-19 onset
  • Geocoded residential history
     
• COVID-19 hospitalization:
  • First hospital admission within -7 days to +15 days after patients’ first COVID-19 positive date

Spatial and contextual exposome measures

• Area- and time-weighted averages:
  • Patients' geocoded residential history within 5 years before COVID-19 positive date
  • 250m circular buffer around each address
     
• A total of 194 exposome factors covering 10 categories

Covariates

• Sociodemographic factors: age, gender, race/ethnicity, health insurance
• Underlying medical conditions:
  • Atherosclerotic cardiovascular disease
  • Myocardial infarction
  • Hypertension
  • Peripheral vascular disease
  • Cerebrovascular disease
  • Diabetes mellitus
  • Chronic obstructive pulmonary disease
  • Asthma
  • Cancer
  • Chronic kidney disease
  • Renal disease
  • Organ transplant
• Time-series county-level COVID-19 related factors:
  • ​Number of days since first COVID-19 case
  • County-level COVID-19 vaccination rates (i.e., at least one dose, fully vaccinated)
  • County-level hospital bed capacity

Statistical analysis

• Phase 1:
  • Mixed effect logistic regression models were fitted for each exposure after adjusting for all the potential confounders with a random intercept by county
  • Benjamin-Hochberg procedure was used to control the false discovery rate (FDR) at 5%
     
• Phase 2:
  • A multivariable mixed-effect logistic regression model including all significant exposures from Phase 1

• Four variables remained significant in Phase 2:
  • 2-Chloroacetophenone
  • Low food access
  • Neighborhood deprivation
  • Density of fitness and recreational sports center
     

• First spatial and contextual ExWAS of COVID-19 hospitalization

• Confirmed a previously reported factor: neighborhood deprivation

• Also generated unexpected predictors that may warrant more focused evaluation

Ackowledgements

• Team:
  • BWH/HMS/HSPH: Yi Zheng, Jaime Hart, Francine Laden, Peter James
  • UF: Jiang Bian, Jennifer Fishe, Jingchuan Guo, William Hogan, Elizabeth Shenkman
• Funding:
  • NIH/NIEHS R21ES032762
  • NIH/NHLBI K01HL153797, OTAHL161847
  • NIH/NCATS UL1TR001427, TL1TR001428, KL2TR001429 
  • PCORI CDRN-1501-26692, RI-CRN-2020-005
  • FDOH 4KB16

Thank you!

hui.hu@channing.harvard.edu

hui-hu.com

github.com/benhhu