Is There Any Relationship Between the Number of COVID-19 Tests by Country with the Income Inequality of Countries? A Comparison with Income Inequality Metrics

OBJECTIVE: Coronavirus diseases 2019 test numbers vary from country to country. The present study aims to investigate whether there is a relationship between the number of coronavirus diseases 2019 tests by country with the income inequality of countries. MATERIAL AND METHODS: The association between the number of coronavirus diseases 2019 tests and income inequality metrics was evaluated in 197 countries. Income inequality metrics consisted of the gross domestic product per capita, Gini index, the P80/P20 ratio, human development index ranking, and poverty rate. The share of the tests that gave positive results (positive rate) and fatality rates was calculated by countries. RESULTS: There was a strong positive correlation between the number of coronavirus diseases 2019 tests per million people and the number of coronavirus diseases 2019 patients per million people (rho = 0.697, P < .001). A significant correlation was observed between the number of coronavirus diseases 2019 tests and all income inequality metrics (P < .001). The strongest positive correlation with the number of coronavirus diseases 2019 tests was between gross domestic product per capita (rho = 0.775), while the strongest negative correlation with the number of coronavirus diseases 2019 tests was between human development index ranking (rho = −0.836). The multiple regression analysis showed that age (P = .01), gross domestic product per capita (P < .001), and human development index ranking were independent factors affecting the number of coronavirus diseases 2019 tests per million people (adjusted R (2) = 0.301). The fatality rate was associated with the number of coronavirus diseases 2019 tests (P = .01). CONCLUSION: Income inequalities in countries are associated with the number of coronavirus diseases 2019 tests. For this reason, many deaths from coronavirus diseases 2019 may have gone overlooked in countries with poor-income inequality metrics.