Skip to main content

Table 2 Adjusted associations between U.S. county race/ethnicity, poverty, economic characteristics, housing/transit characteristics, and population health/health care characteristics with six-month cumulative incidence of coronavirus obtained from negative binomial models (n = 3142)

From: Race, ethnicity, poverty and the social determinants of the coronavirus divide: U.S. county-level disparities and risk factors

 

Model 1 (n = 3141)

Model 2 (n = 3141)

Model 3 (n = 3141)

Model 4 (n = 2956)

IRR (95% CI)

IRR (95% CI)

IRR (95% CI)

IRR (95% CI)

Percent in poverty

1.00

0.99

1.01

0.98

(0.99–1.02)

(0.98–1.01)

(0.99–1.03)

(0.97–1.00)

Race

 Percent White (ref.)

    

 Percent Black

1.03

1.03

1.03

1.03

(1.02–1.03)

(1.02–1.04)

(1.02–1.04)

(1.02–1.03)

 Percent Hispanic

1.02

1.00

1.02

1.02

(1.01–1.03)

(0.99–1.02)

(1.01–1.03)

(1.01–1.03)

 Percent Native American

1.01

1.02

1.01

1.01

(1.00–1.02)

(1.01–1.03)

(1.00–1.02)

(1.00–1.03)

 Percent Asian

1.02

1.00

1.00

1.05

(0.98–1.05)

(0.96–1.03)

(0.97–1.03)

(1.00–1.09)

 Percent two or more races

0.89

0.91

0.90

0.87

(0.85–0.94)

(0.88–0.95)

(0.86–0.94)

(0.84–0.91)

Percent unemployed

 

0.96

  
 

(0.94–0.98)

  

Percent without a high school diploma

 

1.03

  
 

(1.01–1.05)

  

Percent of households with housing cost burden

 

0.99

  
 

(0.97–1.02)

  

Percent of single parent households

 

1.01

  
 

(0.99–1.04)

  

Percent with limited English proficiency

 

1.08

  
 

(0.99–1.19)

  

Percent of crowded households

  

1.01

 
  

(0.95–1.08)

 

Percent of multi-unit households

  

1.02

 
  

(1.01–1.04)

 

Percent of households without vehicle

  

0.98

 
  

(0.97–1.00)

 

Diabetes prevalence

   

1.01

   

(0.99–1.02)

Preventable hospitalization ratea

   

1.00

   

(1.00–1.00)

Percent uninsured

   

1.02

   

(1.00–1.05)

Obesity prevalence

   

1.00

   

(0.99–1.02)

Smoking prevalence

   

1.02

   

(0.99–1.06)

Primary care physician ratea

   

1.00

   

(1.00–1.00)

  1. Models accounted for clustering within states and used ln(population) as the offset. No data was imputed. In addition to the variables listed above for each model, models adjusted for age, sex, rurality, days since county index case and state testing rate
  2. aRate is number per 100,000 population