Who Gets to Live to 100 and Who Doesn’t? Reviewing the 2020 Living to 100 Symposium Monograph

Link: https://www.soa.org/sections/reinsurance/reinsurance-newsletter/2021/march/rsn-2021-03-kaufhold/


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[3] 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

COVID-19 Severity Prediction

Link: https://covidseverity.com//



The Yu Group at UC Berkeley Statistics / EECS / CCB is working to help forecast the severity of the epidemic for individual counties and hospitals in the US. We develop interpretable models (updated daily) and curate data to predict the trajectory of COVID-19-related deaths. This website provides access to those predictions, in the form of interactive visualizations. We are collaborating with Response4Life to blunt the effect of COVID-19 through the production and appropriate distribution of PPE, medical equipment, and medical personnel to healthcare facilities across the United States.

For hospital level prediction, please go to our hospitalization prediction page where one can upload data for a specific hospital and download prediction results for the given hospital. The uploaded data will only be temporarily used for prediction and will not be collected.GITHUB

Author(s): Yu Group, UC Berkeley Statistics / EECS / CCB

Accessed Date: 10 February 2021