Description: Table from an article in The Actuary Magazine on an analysis of risk management terminology: your guide to speaking ERM with different industries. Volumn 4, Issue 6

Description: Supplemental Appendices for Using Social Determinants to Identify Profiles of U.S. Children with Low Dental Expenditures and High Medical Expenditures with Clusters

Description: Data files for presentation.

Description: In this report, we explore the impact of clustering using the Medical Expenditure Panel Study (MEPS) data for children (aged 5 to 17) to build a bridge between oral health and overall health using dental and medical expenditures. We include social determinants of health as cluster input variables using the Partitioning around Medoids (PAM) method to identify children and their families who could potentially benefit from population health management related to oral health.

Description: Provides an overview of the Academy Health organization, its annual research meeting, and the SOA participation at that meeting.

Description: This abstract describes a paper that introduces KDBINTMTM (Knowing, Doing, Being, Inspiring, and Networking), the framework in how it is being defined, and a plan to measure success for actuarial programs.

Description: Full version of Health Section News, Issue No. 54, January 2007.

Description: This is the abstract of a paper that presents a Bayesian model for analyzing time-dependent data. The model allows for the possibility of a shift in the level and/or a shift in the variance of the process at all time points. An example of simulated data will be shown as well as the results of four macroeconomic time series data: inflation, interest, unemployment, and wage. The fitted data of the model is compared against the actual through the use of prediction intervals and the creation of a new measure, called the 'modified Akaike Information Criterion'.

Description: This session takes a fresh look at the issues related to acute care focus and the challenges of providing coordinated care for chronic conditions in the United States. Topics include mental-health disorders, diabetes, cystic fibrosis, From the Record of the Society of Actuaries, Vol. 31, No. 2, Session 89

Description: Abstract from a paper that describes results from a hierarchical Bayesian statistical model which predicts the probability of Non-Acceptable Claim, an unnecessary hospital stay, using hospital discharge claim records as inputs.