country level socioeconomic and health system indicators explain covid-19 mortality worldwide

Background: covid-19 mortality rates differ across countries. we aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and excluding variables related to short-term measures for pandemic management. methods: covid-19 mortality data was collected from johns hopkins university resource center. additional sources were public reports from the united nations, the world bank and the heritage foundation. we implemented multiple linear regression with backward elimination on the selected predictors. results: the final model constructed on seven independent variables, significantly predicted covid-19 mortality rate by country (f-statistic: 29.2, p<0.001). regression coefficients (95% ci) in descending order of standardized effects: annual tourist arrivals: 5.43 (4.03, 6.83); health expenditure per capita: 4.43 (2.92, 5.96); gdp (ppp): -4.60 (-6.81, -2.38); specialist surgical workforce per 100000: 2.63 (0.67, 4.59); number of physicians per 1000: -2.32 (-4.3, -0.28); economic freedom score: -1.35 (-2.60, -0.10); and total population: 1.66 (-0.19, 3.52). all vif values were below 5, showing acceptable collinearity. r-squared (52.65%), adjusted r-squared (50.25%) and predicted r-squared (42.33%) showed strong model fit. conclusion: limited country-level socioeconomic and health system indicators can explain covid-19 mortality worldwide; emphasizing the priority of attending to these fundamental structures when planning for pandemic preparedness.