Description: This article discusses the details of being classified as cognitively impaired, particularly the relationship between being classified as cognitively impaired and subsequent mortality experience and the effect of cognitive impairment on mortality rates.

Description: Discussion of the separate and joint effects of obesity and diabetes on disability and mortality, and ways these effects can be used to improved insurance pricing and reserving, reverse the increasing rates of obesity and diabetes, and improve current projections of disability and mortality risks.

Description: New for 2017, we are introducing a series of articles by experienced industry professionals who are relatively new to long-term care (LTC).

Description: New for 2017, we are introducing a series of articles by experienced industry professionals who are relatively new to long-term care (LTC).

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: State regulators' review of predominant LTC Risks after review of year-end AG51 reports.

Description: Designing and Pricing LTCI Combination Products by Carl L. Friedrich from Long-Term Care Newsletter, December 2004, Issue No. 13.

Description: In 2015, LIMRA partnered with the SOA on a study that focused on examining individual LTC policy termination experience including both voluntary lapse and mortality results. This work was based on data submitted by 22 LTC carriers representing approximately 75 percent of the LTC lives inforce over the study period 2000-2011. This article discusses an area of emphasis for the policy terminations work which was to better understand the impact of data quality on reported results given the continued difficulty for insurers in accurately distinguishing voluntary lapse activity from deaths.

Description: Today, a plethora of tools and approaches exist to develop lifetime projections of long-term care (LTC) business. Within these tools lie two distinct approaches to project mortality; namely, by using assumptions that are applied: (1) to an all-lives exposure base, or (2) separately for disabled- versus active-lives exposure bases. Using disabled versus active mortality allows for more granular modeling of the two different cohorts that exhibit dramatically different mortality. And so, when using an all-lives mortality assumption, is the projection missing important details about the appropriate mortality level? This article seeks to not only answer this question, but also answer how a “traditional” all-lives mortality assumption can be impacted when you introduce a combination of the two modeling techniques into the development of a mortality assumption. To answer these questions, we examined the experience and results developed from one c ompany as an illustrative case study.