Table 3.
Multivariable negative binomial regression analysis on COVID-19 case diagnosis and successful resolution of disease.
Variable1 | RR | SE | (95%CI) |
---|---|---|---|
Cases per million2 | |||
Significant independent variables3 | |||
Days to any lockdown4 | 0.94 | 0.08 | (0.91 to 0.98) |
Days to any border closure5 | 1.04 | 0.02 | (1.01 to 1.08) |
Tests per million population | 1.001 | (< 0.001) | (1.000 to 1.001) |
Median age of population | 1.10 | 0.03 | (1.05 to 1.15) |
Obesity prevalence (%) | 1.06 | 0.027 | (1.01 to 1.11) |
McFadden's Pseudo R^2 6 | 0.091 | ||
Variable7 | RR | SE | (95%CI) |
Recovered cases per million | |||
Significant independent variables3 | |||
Full lockdown (vs. partial/curfew only) | 2.47 | 1.04 | (1.08 to 5.64) |
Days to any lockdown4 | 0.97 | 0.003 | (0.95 to 0.99) |
Adult mortality risk index9 | 0.99 | 0.004 | (0.98 to 1.0) |
GHS Risk Environment (per 10-unit increase)10 | 1.55 | 0.25 | (1.13 to 2.12) |
McFadden's Pseudo R^2 6 | 0.054 |
Abbreviations: RR = rate ratios, SE = standard error, GHS = Global Health Security.
The model exposure variable, required for negative binomial regression analysis of this type, was the duration of virus exposure in days, from the first reported case in the reference country to May 1, 2020.
Dependent variable: cases per million population.
These were the final variables that were retained following the application of the Likelihood ratio test (p < 0.05 to retain) in a backwards elimination process. An RR of less than one means lower risk and greater than one and increased number of events. All continuous independent variables were centered on the mean.
Time to any lockdown from first case in reference country.
Time to any border from first case in reference country.
McFadden's pseudo R-squared is calculated as 1 – LR (full model)/LR (null model). Negative binomial regression does not have an equivalent to the R-squared measure found in ordinary least squares (OLS) regression. Hence, this statistic does not mean what R-square means in OLS regression, which is the proportion of variance for the dependent that is variable explained by the predictor variables. Therefore, the statistic should be interpreted with caution.
Dependent variable: recovered cases per million population.
Probability of dying between 15 and 60 years per 1000 population.
Measured on a scale from 0 to 100 and presents a country's overall risk environment and vulnerability to biological threats. Higher scores indicate reduced vulnerability.