Variations of COVID-19 mortality are affected by economic disparities across countries

Abstract

Background: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge.

Methods: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries.

Interpretation: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

Keywords: COVID-19; Economy; Income levels; Mortality; Policy; Turning points; Waves.

Graphical abstract

Fig. 1

Robust mortality at the case turning point of the COVID-19 pandemic for 103 countries. Panels A, B, and C show mortality based on wave numbers without considering the time sequence. Panels D, E, and F show mortality of waves based on the timeline. Panels G and H show the distribution of mortalities based on wave number and timeline. In panels G and H, the blue, pink, and grey colors represent waves 1, 2, and 3, respectively. Panel I shows the directly calculated and weighted mortality of three waves without the use of corrected data. Panel J shows the directly calculated and weighted mortality of three waves using data from which the outliers had been removed. Panel K shows the mortality of the first three waves, calculated from data after correction based on timeline. Panel L shows the dates of peak mortality for the first three waves, after correcting for both outliers and timeline.

Fig. 2

Turning point mortalities during the first three waves of COVID-19 disease in countries with different income levels. Panels A, C, and E show the average mortalities for waves 1, 2, and 3 in countries of high-, upper middle- and low middle-income, respectively. Panels B, D, and F show the mortalities for waves 1, 2, and 3 in these countries after adjustment with timeline. Panels A1, C1, and E1 show the distribution of mortalities for waves 1, 2, and 3 in countries with different income levels. Panels B1, D1 and F1 show the distribution of mortalities for waves 1, 2, and 3 in these countries after timeline adjustment.

Fig. 3

The similarities and differences in lag times for in countries at different income levels. Panel A shows the lag times of all countries examined during COVID-19 waves 1, 2, and 3. The raw data includes the lag times of all countries, while the “only days <30” data was adjusted to include only lag times that were less than absolute 30 days. Panel B shows lag times for waves 1, 2, and 3 in countries with different income levels, using the “only days <30” dataset Panel C shows the distribution of lag times for countries in the high-income group. Panel D shows the distribution of lag times for countries in the upper middle-income group. Panel E shows the distribution of lag times for countries in the lower middle-income group.

Fig. 4

The relationship between mortality and lag time during waves of the COVID-19 pandemic. Panel A shows the overall relationship between mortality and lag time in all countries during waves 1, 2, and 3. Raw dataset, all data were included; corrected dataset, lag times over 30 days were eliminated. Panel B shows the relationship between mortality and lag time in countries sorted by income level during waves 1, 2, and 3, using corrected data. Panels C, D, and E show patterns and coordinate positions between mortality and lag time in high-income countries during waves 1, 2, and 3. Panels F, G, and H show patterns and coordinate positions between mortality and lag time in upper middle-income countries during waves 1, 2, and 3. Panels I, J, and K show patterns and coordinate positions between mortality and lag time in lower middle-income countries during waves 1, 2, and 3.

Fig. 5

Comparison case number per million during 3 waves of the COVID-19 pandemic in countries with different income levels. A. The average number of cases per million in all countries during waves 1, 2, and 3 using data without timeline correction. B. The average number of cases per million in all countries during waves 1, 2, and 3 using data after timeline correction. Panels C, E, and G show distribution of case numbers in countries with different income levels during waves 1, 2, and 3 without timeline correction. Panels D, F, and H show distribution of case numbers in countries with different income levels during waves 1, 2, and 3 after timeline correction.

Fig. 6

Relation between mortality rates and income levels in the third wave of COVID-19 pandemic. A. Patterns of death rates in the third wave among different income levels. B. The average raw death rates and disease population weighted death rates among three income levels.