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Opioid Dispensing and Group Life Mortality Experience

By Jonathan Polon on 2/13/2020

Key Findings

1. Per-capita levels of opioid dispensing are predictive of county-level group life mortality experience.
2. Area-specific group life mortality risk can vary by age. As a result, carriers may misprice group life insurance for cases where the age distribution differs from average.

Background
From 2010-2017, midlife all-cause mortality rates increased by 6%. A major contributor has been an increase in mortality from specific causes (eg, drug overdoses, suicides, organ system diseases) among young and middle-aged adults. How does this trend affect group life mortality?

Modeling Group Life Mortality Experience: A How-To Guide - Part 4: Validation of Industry and Area Data

By Jonathan Polon on 4/12/2018

Part 4: Validation of Industry and Area Data

Overview

The focus of this, the fourth part of our series on modeling Group Life Mortality Experience, is to continue the data validation of the Basic Life Death and Disability dataset that we started in the previous post.

Specifically, we will focus on the industry (SIC code) and area (zip code) data. The data validation exercise will uncover some concerns about the integrity of the data in these data fields.

Modeling Group Life Mortality Experience: A How-To Guide - Part 3 : Data Validation

By Jonathan Polon on 3/15/2018

Part 3: Data Validation

Overview

The focus of this, the third part of our series on modeling Group Life Mortality Experience, is to review the Basic Life Death and Disability dataset – not yet for analysis but simply to validate the quality of the data.

We will be working with industry data that was provided to the Society of Actuaries (by several contributing Group Life carriers) for its 2016 Group Term Life Experience Study. If you have not yet acquired this dataset, please refer to Part 2 of this series.

Modeling Group Life Mortality Experience: A How-To Guide - Part 2: Acquiring the Data

By Jonathan Polon on 3/2/2018

Part 2: Acquiring the Data

Overview

The focus of this, the second part of our series on modeling Group Life Mortality Experience, is to acquire the dataset that will be the foundation of our model. In this post we will also take a very quick first peek at the dataset but we will hold off on a more thorough exploration of the dataset until the next posting. As a reminder, we will be working with industry data that was provided to the Society of Actuaries (by several contributing Group Life carriers) for its 2016 Group Term Life Experience Study.

Modeling Group Life Mortality Experience: A How-To Guide - Part 1: Introduction

By Jonathan Polon on 2/22/2018

Part 1: Introduction

Introduction

In October 2016, the Group Life Insurance Experience Committee of the Society of Actuaries published the results of the 2016 Group Term Life Experience Study. In June 2017, the Society of Actuaries made available the underlying data at a very granular level.

Percent of US Population by Age Group, 1950-2060

By Jonathan Polon on 3/14/2016
This gif from Pew Research provides a great visualization of the flattening of the age pyramid into an age rectangle that is resulting from decreased birth rates and increase life spans. This really illustrates to insurers and plan sponsors the importance of rethinking their employee benefit programs to help their employees better manage evolving challenges such as elder care, later or gradual retirement and, somewhat paradoxically, longer retirement periods.

How Predictive Analytics Optimizes Employee Engagement in Health Benefits Programs

By Jonathan Polon on 3/3/2016

Great article about a Forrester study about the ways in which predictive analytics can optimize employee engagement and satisfaction with employee benefit plans.

A nationwide study of human resource professionals and employees conducted by Forrester Consulting and commissioned by Evive Health reveals that the effective use of predictive analytics can optimize employee engagement and satisfaction with health benefit programs. Such engagement has been shown to not only save employers significant dollars but can serve as a key differentiator in an organization’s employee retention strategy.

The hidden inequality of who dies in car crashes by By Emily Badger and Christopher Ingraham

By Jonathan Polon on 10/30/2015

Traffic fatalities in the United States have been plummeting for years, a major victory for regulation (strict drunken driving laws have helped) and auto innovation (we have safer cars). But that progress obscures a surprising type of inequality: The most disadvantaged are more likely — and have grown even more likely over time — to die in car crashes than people who are well-off.

New research by Sam Harper, Thomas J. Charters and Erin C. Strumpf, published in the American Journal of Epidemiology, finds that improvements in road safety since the 1990s haven't been evenly shared. The biggest declines in fatalities have occurred among the most educated.