The short-term mortality fluctuation data series, monitoring mortality shocks across time and space

Abstract

The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable than monitoring deaths with COVID-19 diagnosis or calculating incidence or fatality rates affected by numerous problems such as testing coverage and comparability of diagnostic approaches. In response to the emerging data challenges, a new data resource on weekly mortality has been established. The Short-term Mortality Fluctuations (STMF, available at www.mortality.org) data series is the first international database providing open-access harmonized, uniform, and fully documented data on weekly all-cause mortality. The STMF online vizualisation tool provides an opportunity to perform a quick assessment of the excess weekly mortality in one or several countries by means of an interactive graphical interface.

Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries

“…Details on the source and the methodology for collection and standardisation of data from each of these countries or regions have been published previously. 36 37 38 In many countries, age groups originally available in Short-term Mortality Fluctuations data differed somewhat from the required granular age scale in this study. In six countries (Canada, Israel, Germany, New Zealand, South Korea, and the US) Short-term Mortality Fluctuations data included relatively coarse age groups (see supplementary file).…”

“…34 35 Mortality data for 2020 were obtained from the Short-term Mortality Fluctuations data series (a new extension of the Human Mortality Database). 36 For the purposes of this study, we required annual mortality data to be disaggregated by age groups (<1, 1-4, 5-9, . .…”

“…Before calculating observed age specific death rates, we standardised the death counts from Short-term Mortality Fluctuations according to the International Organization for Standardization 8601-2004 guidelines 36 and adjusted for incomplete weekly death statistics in 2020. The age and sex specific adjustment coefficients for incompleteness were calculated using the average annual ratio of the Short-term Mortality Fluctuations data (ie, sum of weekly death counts) to annual death counts during the past five years.…”

“…Details of the methodology have been published previously. 36 We checked the accuracy of life expectancy estimates depending on granular or broad age intervals in the Short-term Mortality Fluctuations data and found only small deviations, even for broad age scales (see supplementary file for details of the methodology, including sensitivity analysis, and supplementary figures S1 and S2). For 2015 data, we used annual death counts and population exposures by five year age groups from the Human Mortality Database.…”

Life expectancy declines in Russia during the COVID-19 pandemic in 2020

“…Period life expectancy witnessed declines in 2020 at a scale that is unprecedented in recent history. With the recent availability of data from Russia (Jdanov et al, 2021), we show that Russia is no exception. Data from death registrations indicate that life expectancy between 2019 and 2020 in Russia fell by -1.68 (95% Confidence interval (CI): -1.72,-1.63) years among males (from 68.4 to 66.7 years) and -1.80 (95% CI: -1.85,-1.76) years for females (from 78.4 to 76.6 years).…”