This is a cross-sectional study in which we utilized publicly available data of COVID-19 cases for each country and territory. We combined these data with countries' publicly available information about demographics and socioeconomic indicators published in different sources. Overall, we included 211 countries and territories. We excluded countries with less than 100 cases reported by April 6, 2020. Statistical models were built to identify factors associated with each outcome variable, including the incidence rate, recovery rate, rate of critical cases, and mortality rate.
The incidence rate of COVID-19 was obtained using country reported cases and country population size from the Worldometer website . The Worldometer specifies the source of each country data, which are based on official media venues, and has been used in previous publications . The incidence rate per 1000 was calculated using the equation: (total cases/ population size) *1000.
The COVID-19 mortality rate was calculated using the country reported deaths of COVID-19 and divided by the population size . Thus, mortality rate per 1000= (total deaths / population size) * 1000. The rate of critical cases rate was calculated using country reported critical cases divided by the total confirmed cases . The recovery rate was calculated using reported cases that recovered divided by the population size .
The demographic and socioeconomic indicators analyzed in this study include population size, urban population rate, median age, fertility rate, population density, land area, life expectancy, gross domestic product (GDP), tourism indicator, and the 2019 Global Health Security Index (GHS). The GHS index is a comprehensive assessment of the global health security capabilities of 195 countries, where a score of 100 points is the highest . The index is focused on the capabilities of countries to predict, prepare for, prevent, and respond to infectious disease outbreaks as well as the health system, commitment to improving, and vulnerability to biological risk.
The explanatory variables were collected from various sources and included population size (2020), yearly population change (%), net population change, population density (P/Km²), land area (Km²), migrants (net), fertility rate, median age, life expectancy for each gender and overall, the urban population in percentage, and world share in the population. These variables were based on the latest United Nations Population Division estimates . The country-level factors, including the published number of COVID-19 tests per million for each country, the 2017 gross domestic product (GDP), GDP growth, GDP per capita, and share of World GDP, were based on the World Bank and the United Nations data . Moreover, we included the 2018 international tourism indicator for each country. The 2018 international tourism indicator is based on the total number of travelers who travel into a country other than their country of residence obtained from the World Bank data .
All analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). The analyses performed include the incidence rate, recovery rate, rate of critical cases, and mortality rate. For the incidence rate and mortality rate, we applied the log-transformation to approximate the normal distribution. For the rate of critical cases and recovery rate, we applied the logit transformation to obtain the appropriate distributions of the linear regression.
For all explanatory variables in the model, we standardized variables with mean=0 and standard deviation=1. This was done to address the multicollinearity in the models. Next, we constructed a stepwise linear regression model for each outcome variable to build the final model and estimate the coefficient of each explanatory variable. In all models, we excluded countries with less than 100 cases of COVID-19 to omit outliers. Thus, the sample sizes for incidence rate, recovery rate, rate of critical cases, and mortality rate are 93, 96, 90, and 94 counties, respectively. The level of significance was declared at alpha (α) = 0.05. Because the study used publicly available information a country level without individual identifiers, it was exempt from IRB review.