Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.

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

Description: Research report describing an analysis of experience transition rates between Long Term Care insurance claim settings over the course of a claim. Examples of such transitions are from an assisted care facility to home health care or from home health care to a nursing home.

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.