As covid-19 has spread around the world, people have become grimly familiar with the death tolls that their governments publish each day. Unfortunately, the total number of fatalities caused by the pandemic may be even higher, for several reasons. First, the official statistics in many countries exclude victims who did not test positive for coronavirus before dying—which can be a substantial majority in places with little capacity for testing. Second, hospitals and civil registries may not process death certificates for several days, or even weeks, which creates lags in the data. And third, the pandemic has made it harder for doctors to treat other conditions and discouraged people from going to hospital, which may have indirectly caused an increase in fatalities from diseases other than covid-19.
One way to account for these methodological problems is to use a simpler measure, known as “excess deaths”: take the number of people who die from any cause in a given region and period, and then compare it with a historical baseline from recent years. We have used statistical models to create our baselines, by predicting the number of deaths each region would normally have recorded in 2020 and 2021.
Many Western countries, and some nations and regions elsewhere, regularly publish data on mortality from all causes. The table below shows that, in most places, the number of excess deaths (compared with our baseline) is greater than the number of covid-19 fatalities officially recorded by the government. The full data for each country, as well as our underlying code, can be downloaded from our GitHub repository. Our sources also include the Human Mortality Database, a collaboration between UC Berkeley and the Max Planck Institute in Germany, and the World Mortality Dataset, created by Ariel Karlinsky and Dmitry Kobak.
Work accomplished under the 2019 agreement between the Human Mortality Database (HMD) team and the Society of Actuaries (SOA) was divided between two main projects: 1) the continuous development of the United States Mortality Database (USMDB) and 2) the publication of cause-specific mortality series for selected HMD countries. Due to administrative delays at both the University of California, Berkeley, and the Society of Actuaries, work on these projects did not begin until July 2019. Furthermore, due to restriction in data access associated with the Covid-19 pandemic, a no-cost extension was requested by Magali Barbieri, the Principal Investigator for the projects, and accepted by the SOA to extend the project beyond the initial December 31, 2019 deadline.
Author(s): Magali Barbieri, Ph.D University of California-Berkeley
Given these advances in understanding the theoretical methods of evaluating multiple, related mortality data sets, it is particularly promising that the Human Mortality Database, with the SOA’s sponsorship, has recently made available mortality data for the United States at the level of the individual county. Moreover, Professor Magali Barbieri of University of California, Berkeley in January 2021 published an SOA Research Report on “Mortality by Socio-economic Category in the United States” using this data series. Professor Barbieri is one of the directors of the HMD project, which is jointly run by UC Berkeley and the Max Planck Institute for Demographic Research in Rostock, Germany and support from the Center on the Economics and Development of Aging (CEDA) and the French Institute for Demographic Studies (INED). In her paper, Barbieri studies socio-economic differences linked to mortality differentials by county, based on information available at the county level regarding education, occupation, employment, income, and housing. The gap between the highest and lowest county decile is huge and growing. In 2018, the qx-rate for 45-year-old men in counties with the lowest Socioeconomic Index Score (SIS) was 2.5 times that for men of that age in counties with the highest SIS. This gap is even greater than the difference between smokers and non-smokers. Professor Barbieri’s report shows the widening trend between the different socio-economic strata which she captures by grouping the counties into deciles by SIS. While the highest SIS score is associated with a life expectancy that matches or even beats the OECD average, people living in counties with the lowest SIS have hardly seen any improvement in their life expectancy over the last four decades. Comparing the average life expectancy at birth within the highest decile of counties to the lowest, there was a gap of 3.0 years in 1982, the first year for which consistent data was available. This gap has more than doubled since then, rolling in at 6.6 years difference in life expectancy in 2018. That is an increase of 120 percent. Worse still, the gender gap once again manifests itself in the mortality trends, with females showing an increase of the socio-economic mortality gap of 260 percent over the 36-year period, compared to 76 percent for males.
Author(s): Kai Kaufhold
Publication Date: March 2021
Publication Site: Reinsurance News at the Society of Actuaries