Predictors of COVID-19 testing rates: A cross-country comparison.

Abstract Objectives Cross-country comparisons of COVID-19 have largely been applied to mortality analyses. The goal of this analysis is to explore predictors of COVID-19 testing through cross-country comparisons, in order to better inform testing policies. Methods Testing and case-based data was amassed from Our World in Data, and information regarding predictors was gathered from the World Bank. We investigate Human Development Index (HDI), health expenditure, universal health coverage (UHC), urban population, service industry workers (%), and air pollution as predictors. We explored testing data through July 31st, 2020, or most recently available, using case-indexing methods which involve synchronizing countries by date of first reported COVID-19 case as an index date and normalizing to the cumulative tests 25 days post-index date. Three multivariable linear regression models were built in a stepwise fashion to explore the association between the indexed number of COVID-19 tests and HDI scores. Results A total of 86 countries were included in the final analytical sample, excluding countries with missing data. HDI and urban population were found to be significantly associated with testing. Conclusions Results suggest that social conditions and government capacity remain consistently salient in the consideration of testing rates.