Table 2.
Multiple regression for predicting Covid-19 mortality rates.
| Predictors | RRa | SEb | P | 95% CI |
|---|---|---|---|---|
| Test number per 100 people | 0.92 | 0.02 | 0.001 | 0.87–0.96 |
| Case number per 1,000 people | 1.03 | 0.04 | 0.477 | 0.95–1.10 |
| Critical case rate (%) | 1.05 | 0.06 | 0.372 | 0.94–1.18 |
| Government effectiveness scorec | 0.96 | 0.02 | 0.017 | 0.92–0.99 |
| Population aged 65 or older (%) | 1.12 | 0.02 | < 0.001 | 1.07–1.17 |
| Bed number per 1,000 people | 0.85 | 0.03 | < 0.001 | 0.80–0.90 |
| Communicable disease death rate (%) | 0.99 | 0.01 | 0.157 | 0.98–1.00 |
| Transport infrastructure quality scored | 1.08 | 0.03 | 0.002 | 1.03–1.14 |
A total of 101 countries were included in the regression analysis. The dependent variable was Covid-19 mortality rate % (log). The R-squared value was 0.58; adjusted R-squared value was 0.54.
aRR: relative risk. bSE: standard errors. c,dBoth government effectiveness and infrastructure quality scores were multiplied by 10. Thus the corresponding relative risk should be interpreted on the basis of a 0.1 incremental increase in these indicators.