Description: This report contains a set of of unloaded and loaded mortality tables for guaranteed issue business.

Description: In summer 2021, the SOA Research Institute’s Mortality and Longevity Strategic Research Program sponsored a webcast examining individual life and group life emerging mortality in a COVID-19 world. Since then, Delta and Omicron coronavirus variants have fueled waves of the virus in the U.S., boosting the total number of COVID-19 deaths since the beginning of the pandemic to near the 1 million mark. Hear an update and insights into how the life insured population is faring during COVID-19 by discussing the findings from two recent SOA Research Institute initiatives. LIMRA, Reinsurance Group of America (RGA), the Society of Actuaries (SOA) and TAI have collaborated to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience. Additionally, the SOA Research Institute has surveyed insurers to gather a high-level view of U.S. Group Term Life Insurance mortality results during the COVID-19 pandemic, comparing them to pre-pandemic mortality levels. The Group Term Life industry mortality results will be presented with monthly data through March 2022.

Description: For decades, drug overdose deaths have been increasing within the U.S., with opioid-related mortality driving this increase. The Society of Actuaries Research Institute (SOA) sponsored this research to investigate the trends and drivers behind drug overdose mortality and substance-related deaths as a whole. Findings provided a range of insights surrounding substance use disorder, drug overdose and alcohol-induced mortality, and considerations for insurers.

Description: The Mortality Improvement subgroup of the American Academy of Actuaries’ Work Group and the Society of Actuaries’ Mortality and Longevity Oversight Advisory Council released a recommendation for a set of improvement factors that vary by gender and attained age to be used in conjunction with the 2008 Valuation Basic table or the 2015 Valuation Basic table for AG-38 purposes. This recommendation has been adopted by the NAIC’s Life Actuarial Task Force.

Description: Our understanding of the biology of aging is increasing rapidly, creating insight into how and why any organism is aging. Some of the mechanisms by which the process of aging occurs in mammals are sufficiently well understood that clinical trials targeting them in humans to improve later life health and longevity are now being undertaken. Translating results seen in mammals to humans has the potential to increase life expectancy far beyond what we have seen historically. Come explore the current state and outlook of research.

Description: "Demographic components of Future Potential Old-Age Support Ratios – Kirill Andreev, Ph.D., United Nations, Population Division, New York, Mila Andreeva, KAMA Consulting Effect of internal Migration on the Health of Taiwan’s Elderly People – Jack C. Yue, Ph.D., – National Chengchi University, Department of Statistics, Taipei, TaiwanPopulation Aging and Canada’s Social Contract: Covid-19 Catalyst for Change – Douglas Andrews, FSA, FCIA, Ph.D., Lori J. Curtis, Ph.D., – University of Waterloo"

Description: "Establishing Methods for Annual Updates to Cohort Life Expectancies – R. Dale Hall, FSA, CERA, MAAAManaging Director of Research, Society of Actuaries Research Institute Longevity Disparity across Communities in the United States – Lijia Guo, Ph.D., ASA, MAAA , US Government Accountability Office, Applied Research and Methods Division, Washington DC"

Description: Both the SOA and AARP will showcase their recent research projects highlighting the impact of marginalization on aging and retirement. Learn how being a member of a marginalized group changes your outcomes as well as your perspective on aging and retirement.

Description: An analysis of US population mortality trends and the impact of COVID.

Description: Mortality Model Selection and Combination in RAlternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base mortality forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation and other alternative out-of-sample measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. Such methods combine predictions from multiple mortality models to reduce model choice uncertainty and improve forecast accuracy. Explore techniques to select and combine mortality models using the freely available open source R package CoMoMo (https://github.com/kessysalvatory/CoMoMo), which extends the widely used R package StMoMo (https://CRAN.R-project.org/package=StMoMo). We will use data for the USA and other countries to discuss and demonstrate four different model combination approaches including simple model averaging, Bayesian model averaging, model confidence set, and stacked regression ensembles.