Predictors of COVID-19 Mortality: A Stratified 3 by 3 Factorial Correlation Analysis
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In this paper the strength of association of some factors with COVID-19 mortality across countries is examined. This is essential for future pandemic preparedness. In this study, a 3 × 3 factorial design where 36 countries were stratified by GDP per capita and population density was used. Secondary data were sourced from the World Bank, United Nations, and Our World in Data repositories. A parsimonious linear regression model with four predictors: positivity rate, vaccination coverage, log (GDP per capita), and median age was fitted. Bootstrapping provided 95% confidence intervals. Low-GDP countries had higher positivity rates (10.2% vs. 3.0%) but lower reported deaths (167 vs. 1,186 per million) than high-GDP countries. The model explained 89.2% of variance (adjusted R² = 0.878, p < 0.001). Positivity rate was the strongest predictor of mortality (β = 10.51, p < 0.001), followed by GDP per capita (β = 1.39, p < 0.001). The positivity-deaths correlation was strongest in high-GDP countries (r = 0.898, p < 0.001) compared to low-GDP countries (r = 0.597, p = 0.019), suggesting that differential death reporting attenuates associations in low-resource settings. These findings suggest positivity rate as a high value predictor of COVID-19 mortality. Maintaining low positivity rates through accessible testing should guide future pandemic surveillance.
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