Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"

Description: This article discusses the problems in estimating the prevalence of HIV infection.

Description: Healthcare costs have been rising faster than general inflation for many years. Early models that predicted this past pattern projected healthcare expenditure growth that continued to outpace total expenditure growth leading to the fallacious position that the whole of the US economy would ultimately be consumed by healthcare. The Society of Actuaries' Pension Section and Health Section Research teams commissioned Professor Thomas E. Getzen of Temple University to construct a resource model for the projection of long term healthcare cost trends that includes capacity constraints to ensure the long-term projection is rational. The motivation for creation of the model was driven by its potential usage as a resource for the estimation of reportable liabilities for retiree healthcare benefits as specified under FAS 106 and GASB 45 accounting statements. This is a zip file containing the following: 1. A background paper on the creation of the health trend model, 2. The model, programmed in Microsoft Excel [xls format], that projects per-person expenditures and growth rates through 2099 using baseline assumptions, but also permits the flexibility for user–inputted alternative assumptions, and 3. Technical documentation providing transparency and support for the ultimate results.

Description: Actuaries historically modeled new launches in pharmaceuticals by anticipating the expected change to the market basket. This became more complicated with the recent launches of gene and cell therapies as they address rare diseases. Rare conditions have low total member volume to study and some do not currently have a diagnosis code to identify them within claims data. Addressing this modeling challenge has been growing more important in the recent years with the number of therapies launching. What has the health care industry, and more specifically health care, done to enhance modeling and how should this be altered for the future? Additionally, historic modeling has sometimes incorporated a level of direct medical treatment cost offsets where studies have shown lower treatment costs are achievable. Can gene and cell financial modeling include cost offsets as well? And if so, what are suggested precautions or limitations to this effort?

Description: MHPAEA prohibits health plans from imposing financial requirements or treatment limitations on behavioral health benefits that are more restrictive than those applied to medical and surgical benefits. MHPAEA's regulations define treatment limitations to include nonquantitative treatment limitations (NQTLs), which are benefit design features that are not numerical, but otherwise limit the scope or duration of benefits.

Description: Healthcare costs have been rising faster than general inflation for many years. Early models that predicted this past pattern projected healthcare expenditure growth that continued to outpace total expenditure growth leading to the fallacious position that the whole of the US economy would ultimately be consumed by healthcare. The Society of Actuaries' Pension Section and Health Section Research teams commissioned Professor Thomas E. Getzen of Temple University to construct a resource model for the projection of long term healthcare cost trends that includes capacity constraints to ensure the long-term projection is rational. The motivation for creation of the model was driven by its potential usage as a resource for the estimation of reportable liabilities for retiree healthcare benefits as specified under FAS 106 and GASB 45 accounting statements. This is a zip file containing the following: 1. A background paper on the creation of the health trend model, 2. The model, programmed in Microsoft Excel [xls format], that projects per-person expenditures and growth rates through 2099 using baseline assumptions, but also permits the flexibility for user–inputted alternative assumptions, and 3. Technical documentation providing transparency and support for the ultimate results.

Description: The Actuary Vol. 22, No. 6. The author provides commentary regarding an interview with Paul Barnhart reported on in The Actuary March 1988 concerning the controversy over health insurance reserves. Specifically the elements of the “Benefit Ratio Reserve” method were analyzed.

Description: This is the complete January 2012 issue of Health Watch.

Description: This is the Introductory Study Note for the Society of Actuaries Group and Health-Design and Pricing examination for Spring 2012 that will be given on Thursday, April 26th, from 8:30 a.m. – 11:45 a.m. and 1:30 p.m. – 4:45 p.m.

Description: The Society of Actuaries' Pension Section and Health Section Research teams commissioned Professor Thomas E. Getzen of Temple University to construct a resource model for the projection of long term healthcare cost trends. The motivation for creation of the model was driven by its potential usage as a resource for the estimation of reportable liabilities for retiree healthcare benefits as specified under FAS 106 and GASB 45 accounting statements. This model, programmed in Microsoft Excel [xlsx format], projects per-person expenditures and growth rates through 2099 using a set of equations and assumptions developed by Professor Getzen with assistance from the Society of Actuaries Project Oversight Group appointed to oversee the effort. The model includes baseline assumptions as well as flexibility for user–inputted alternative assumptions.