Description: Table constructed from lifetime long-term care cost studies conducted by P.J. Eric Stallard, ASA, FCA, MAAA, Center for Demographic Studies, Duke University.

Description: This article explores the possibility of using innovations from the financial markets to reduce some portion of the LTC risk and thus encourage the growth of the product.

Description: In the United States, individuals needing long-term services and supports (LTSS) access care, utilize services, and pay for their services in various ways. The authors explore how the need for, utilization of and payment for long-term services and supports differ in the U.S. by population cohorts. Their primary focus is disparity by race and ethnicity, but they also consider variations by geography, family structure, age, sex, gender and other socioeconomic factors.

Description: Read the November 2020 issue of Long-Term Care News published by the Long-Term Care Insurance Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Recommendation to measure the risk for the components of combination Life/LTC products.

Description: As the topic of affordable long-term care (LTC) insurance comes into focus in political conversations, we often find ourselves thinking, “What is going to happen to the private LTC insurance industry going forward?” The need for LTC in the United States is not going away, especially as the population continues to age. With this in mind, this article explores three possible future paths for LTC insurance funding and the associated implications for the private LTC industry.

Description: Understanding How Families Search for Long-Term Services and Supports

Description: This fact sheet provides an overview of the Long-Term Care and Middle Market report.

Description: Informal caregiving is a major source of long-term care (LTC) and long-term supports and services (LTSS) more broadly. The benefit of informal caregiving is appreciated by those receiving it, and most caregivers find purpose in their work. The costs of caregiving fall on employers, other family members, the health care service sector, stakeholders in the LTSS system, and most importantly the caregivers themselves. Providing support to these caregivers can therefore benefit employers, employees, and their families, as well as the many payors of formal LTSS. Hear from three experts in caregiving who will provide their perspective about how to support information caregivers — operationally and financially — to help them succeed in their purpose. Understand benefits this support can have to the other stakeholders in today’s LTSS system. By attending this session, you will be able to: • Explain the importance of informal caregivers to stakeholders in the LTSS system, including the impact to payors and employers. • Describe the challenges informal caregivers face, and paths to success and avoiding burnout. • Discuss potential future financial solutions that may benefit caregivers, including employee programs and insurance opportunities.

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.