To the best of our knowledge, this is the first country level study to systematically examine the factors related to Covid–19 mortality. We found that Covid–19 test number is negatively associated with mortality rates. The effectiveness of population screening for Covid–19 infection to reduce mortality risk is currently being debated. Those supporting screening suggest the beneficial effect of identifying asymptomatic patients to attenuate Covid–19 spread. Opponents argue that reduced mortality risk is mainly due to increased detection of asymptomatic patients. In the present study, we found that one additional test per 1,000 population is associated with a 3% reduction in mortality rate, even after adjusting for case number, critical case rate, and other country-related factors.
We also found that Covid–19 case number and critical case rate are positively associated with mortality risks. This is consistent with the real-world observations that high case numbers and high critical rates have led to health care system overload in several countries. Covid–19 infected patients need greater health care resources, such as isolation rooms and health care faculties and facilities. The availability of health care resources has been reported to be associated with Covid–19 mortality . Attenuating increases in case number and critical case rate may help to increase Covid–19 patient survival rates.
Recent Covid–19 clinical studies have reported associations for mortality with old age and multiple comorbidities [6,7,23]. We confirmed these observations. Countries with higher proportions of people aged 65 or older had significantly higher mortality rates. In the present study, bed number was not significantly associated with Covid–19 mortality rate. Although one additional bed number per 1,000 population was negatively associated with a 7% reduction in risk of mortality, this association was not significant. A potential reason for this non-significance is the wide variation in bed vacancy rates and bed types. However, this information was not available from the WDI database. The influence of other stronger factors, such as critical case rate, test number, and government effectiveness, may have also contributed to the non-significance. An alternative explanation is that several countries have constructed temporary hospitals to increase bed numbers, not recorded in the WGI database, which may have affected the importance of bed number as a predictor of mortality rates.
Government effectiveness is a measure of good governance, which is essential to long-term development outcomes . In the present study, we demonstrated that for short-term crises such as the Covid–19 outbreak, government effectiveness remains critical, as it related to the formulation and implementation of quarantine and screening policies, as well as quality of public health services in managing and treating patients. In addition, we found that communicable disease death rate, an indicator of the ability of the countries to prevent and treat infectious diseases, is positively associated with Covid–19 mortalities.
Positive test rate is a combination of the effects of case number and test number. A higher positive test rate implies under-screening with some asymptomatic patients going undiagnosed. These asymptomatic patients may increase the risk of Covid–19 spread and case number. Moreover, not counting these asymptomatic patients leads to over-reporting of critical case rates and mortality rates. These factors may explain the positive association between positive test rate and mortality rate.
There are several limitations to the present study. First, this study is based on Covid–19 cases reported by countries. Inaccurate reporting and the rapid increases in cases may have influenced the predictive power of our model. However, the trends in the prognostic factors for predicting mortality rates may not have changed. Second, the lack of completeness of the database limits our analyses in certain countries, for example test numbers in China and critical case numbers in India. Third, the Covid–19 related factors used in the present study are from country-level data, not patient-level data. If worldwide patient-level data is made available for analyses, the prediction accuracy will further improve. Fourth, we selected only a limited number of factors that potentially determine the Covid–19 mortality in a country.
Future studies may explore other country-related factors to improve the prediction accuracy. Finally, acquired community immunity after the worldwide spread of Covid–19 may change the prediction accuracy. However, the results of this study can still contribute to future pandemic-related policymaking at the country level.
In conclusion, we found that higher Covid–19 mortality is associated with lower test number, higher critical case rate, higher case number, lower government effectiveness, aging population, and higher communicable disease death rate. Increasing Covid–19 test number, reducing critical case rate, and improving government effectiveness have the potential to reduce Covid–19 related mortality.