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Table 2 Predictors of testing prevalence and positive test prevalence over time

From: Analyzing disparities in COVID-19 testing trends according to risk for COVID-19 severity across New York City

 

Total Tests / Populationa

Timeframe

March 2nd to April 6th

April 6th to May 12th

May 12th to July 6th

 

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

White residents (%)

1.0003 (1.0003–1.0003)

1.0003 (1.0003–1.0004)

1.000 (0.999–1.000)

0.999 (0.999–1.000)

1.0003 (1.0003–1.0003)

1.000 (0.999–1.000)

Hispanic composition (%)

0.999 (0.999–1.000)

1.0001 (1.0001–1.0002)

0.999 (0.999–1.000)

1.000 (0.999–1.000)

0.999 (0.998–0.999)

1.000 (0.999–1.000)

Median age (years)

1.001 (1.000–1.001)

1.0007 (1.0005–1.0009)

1.004 (1.002–1.006)

1.005 (1.003–1.007)

1.004 (1.003–1.005)

1.004 (1.002–1.005)

COVID risk index quartiles

1.002 (0.996–1.009)

1.038 (1.029–1.046)

1.038 (0.971–1.109)

1.017 (0.939-1.101)

0.834 (0.797–0.875)

0.862 (0.814–0.913)

 

Positive Tests / Total Testsb

Timeframe

March 2nd to April 6th

April 6th to May 12th

May 12th to July 6th

 

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

IRRunadj (95% Confidence Limits)

IRRadj (95% Confidence Limits)

White residents (%)

1.000 (0.999–1.000)

0.999 (0.999–0.999)

0.999 (0.999–1.000)

0.999 (0.999–0.999)

0.999 (0.999–0.999)

0.999 (0.998–0.999)

Hispanic composition (%)

1.000 (1.000–1.000)

1.000 (0.999–1.000)

1.001 (1.000–1.001)

1.000 (1.000–1.000)

1.001 (1.001–1.002)

1.000 (1.000–1.000)

Median age (years)

0.999 (0.999–1.000)

0.999 (0.999–1.000)

1.000 (0.999–1.000)

1.001 (1.001–1.002)

0.998 (0.997 – 0.999)

1.001 (0.998–1.003)

COVID risk index quartiles

1.056 (1.037–1.077)

1.010 (0.987–1.034)

1.108 (1.083–1.133)

1.031 (1.002–1.060)

1.248 (1.164–1.338)

1.135 (1.042–1.237)

  1. aPoisson regression performed, adjusted with a Pearson scaling factor to correct for overdispersion, log (population) used as an offset
  2. bPoisson regression performed
  3. Results shown for unadjusted and adjusted models. Models were adjusted for all variables shown. The total tests / population was calculated per hundred residents. A larger risk index quartile represents higher risk. Results for white residents (%), Hispanic composition (%) and Median age are reported in units of 10. IRR: incidence rate ratio