The total amount of excess mortality will also depend on the age structure of a population. Countries with age structures weighted towards an older population will experience higher mortality than a country with an age structure weighted towards a younger population. We calculated the expected mortality for each country by taking the average of the past 5 years . We calculated sex-specific age-adjusted excess mortality rates by standardising to the European Standard Population using age-groups of .
Data are presented as age-standardised total mortality per 100,000, age-standardised total excess mortality per 100,000 and percentage increase in mortality per age-adjusted 100,000. Relative increases are useful when comparing countries with marked differences in annual mortality rates. We built a web-based application that allows the user to graph and tabulates excess mortality statistics for each country using an average of the previous 5-year data. In the table, below we summarise the data for age-standardised mortality for 2020 calculating expected age-standardised mortality using an average of the previous 5-year’s data where possible and the average of 4 years where that data were not available , using an alternative web-based application that we’ve built.
A number of eastern European countries saw little or no excess deaths in the first half of the year but have experienced significant excess mortality in the second half of 2020. Bulgaria, Czechia, Croatia, Hungary, Lithuania, Luxembourg, Poland, Slovakia, and Slovenia with Poland and Bulgaria exhibiting levels of excess mortality of the same order of magnitude as the countries in the centre of the first wave . The USA which has often been cited as the worse affected country has relative excess of 12.9% which although one of the highest, is below some with even higher relative excess mortality such as Poland and Chile. Relative standardised excess mortality is one method of measuring the impact of the SARS-nCOV2 pandemic.
In addition, using simple averages of historical mortality data could underestimate if there is a significant downward trend in mortality or overestimated if there are upward trends.
Source: Ufuk Parildar, Rafael Perara, Jason Oke | CEBM.net