A question central to the Covid-19 pandemic is why the Covid-19 mortality rate varies so greatly across countries. This study aims to investigate factors associated with cross-country variation in Covid-19 mortality. Covid-19 mortality rate was calculated as number of deaths per 100 Covid-19 cases. To identify factors associated with Covid-19 mortality rate, linear regressions were applied to a cross-sectional dataset comprising 169 countries. We retrieved data from the Worldometer website, the Worldwide Governance Indicators, World Development Indicators, and Logistics Performance Indicators databases. Covid-19 mortality rate was negatively associated with Covid-19 test number per 100 people (RR = 0.92, P = 0.001), government effectiveness score (RR = 0.96, P = 0.017), and number of hospital beds (RR = 0.85, P < 0.001). Covid-19 mortality rate was positively associated with proportion of population aged 65 or older (RR = 1.12, P < 0.001) and transport infrastructure quality score (RR = 1.08, P = 0.002). Furthermore, the negative association between Covid-19 mortality and test number was stronger among low-income countries and countries with lower government effectiveness scores, younger populations and fewer hospital beds. Predicted mortality rates were highly associated with observed mortality rates (r = 0.77; P < 0.001). Increasing Covid-19 testing, improving government effectiveness and increasing hospital beds may have the potential to attenuate Covid-19 mortality.

Covid-19大流行的核心问题是为什么Covid-19死亡率在各个国家之间差异如此之大。这项研究旨在调查与Covid-19死亡率越野变化相关的因素。将Covid-19死亡率计算为每100例Covid-19病例中的死亡人数。为了确定与Covid-19死亡率相关的因素，将线性回归应用于包括169个国家的横截面数据集。我们从Worldometer网站，全球治理指标，世界发展指标和物流绩效指标数据库中检索了数据。Covid-19死亡率与每100人的Covid-19测试次数（RR = 0.92，P  = 0.001），政府有效评分（RR = 0.96，P）呈负相关 = 0.017）和病床数（RR = 0.85，P  <0.001）。Covid-19死亡率与65岁或65岁以上人口的比例（RR = 1.12，P  <0.001）和交通基础设施质量得分（RR = 1.08，P  = 0.002）呈正相关。此外，在低收入国家和政府有效性评分较低，人口年轻且病床较少的国家中，Covid-19死亡率与检测数量之间的负相关性更强。预测的死亡率与观察到的死亡率高度相关（r = 0.77；P  <0.001）。增加Covid-19的检测，提高政府效力和增加病床可能会降低Covid-19的死亡率。