Insurance Medicine

Actual to expected deaths rates for HIV

I want to use this post as an opportunity to show how an insurance medicine physcian looks at the literature.  Basically, we compare the actual or observed death rates by age and with the expected death rates based on something like the US Vital Statistics. 

I was recently retained (by a lawyer) to review the insurability of people infected with HIV.   Clearly HAART treatments are making great strides and improving mortality, but what does current mortality actually look like?

I took a recent article Bhaskaram, et al, "Changes n the risk of Death After HIV Seroconversion Compared with Mortality in the General Population," JAMA July 2, 2008, Vol 300 p 51.  and applied the actual to expected methodology.  I think you may find the results interesting.  I look forward to your comments.   

Observed to general population mortality results by age and duration
Table 4 also can also be used to show observed to expected mortality results which are surprisingly favorable.   In order to best apply this study to life insurance (actuarial) methodology, I translated the study’s “overall probability of deaths in percent” in each age and duration group to deaths per thousand.  I used the 2004 to 2006 period to reflect the most current treatment status.  I compared the number of deaths in the article in the article to the number of expected deaths by age in the 2004 US Vital Statistics http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf.     (Note that I substituted US for European mortality to make these calculations.)

Comparing the most current treatment mortality data, that from 2004 to 2006, with US life table mortality (referenced above), gives the following charts:

Mortality for 2004 to 2006 Period for Those Infected 5 Years (study “overall probability %” translated to deaths per 1000)
Age group	From the study, observed deaths expressed  per 1000 for years 2004 to 2006	From the study, observed deaths per 1000 per year (n/3)	From US Vital Statistics, expected deaths per 1000 per year, average age from column 1 + 5 years (rounded).	Ratio of observed deaths in the study to expected deaths from US population as a % (total, not excess deaths)
15 to 24	0	0	1	< 100 %
25 to 34	3	1	1	100 %
35 to 44	8	3	3	100 %
≥ 45	49	16	6	267%

Mortality for 2004 to 2006 Period for Those Infected 10 Years (study “overall probability %” translated to deaths per 1000)
Age group	From the study, observed deaths expressed  per 1000 for years 2004 to 2006	From the study, observed deaths per 1000 per year (n/3)	From US Vital Statistics, expected deaths per 1000 per year, average age from column 1 + 10 years (rounded).	Ratio of observed deaths in the study to expected deaths from US population as a % (total, not excess deaths)
15 to 24	54	18	1	1800 %
25 to 34	20	6.7	2	335 %
35 to 44	37	12.3	4	307 %
≥ 45	122	47	6	783 %

Mortality for 2004 to 2006 Period for Those Infected 15 Years (study “overall probability %” translated to deaths per 1000)
Age group	From the study, observed deaths expressed  per 1000 for years 2004 to 2006	From the study, observed deaths per 1000 per year (n/3)	From US Vital Statistics, expected deaths per 1000 per year, males, average age from column 1 + 15 years (rounded).	Ratio of observed deaths in the study to expected deaths from US population as a % (total, not excess deaths)
15 to 24	81	27	1	2700 %
25 to 34	75	25	3	833 %
35 to 44	72	24	8	300 %
≥ 45	249	83	16	519 %

I suspect that some of the volatility in observed to actual percents is due to small numbers of patients in the groups.    

The coming tsumani of diabetes and increased mortality

This week’s Lancet contains an extremely important and well written article on the steeply increasing incidence (new cases per year) and prevalence (total cases at one time) of diabetes in a population study from Ontario Canada between 1995 and 2005. 

This obesity-diabetes epidemic will definitely  impact the previously increasing life expectancy.  Dr. Olshansky et al. were correct when they predicted that obesity epidemic may cause a decline in the life expectancy.  The key findings are:

1.   

The prevalence of diabetes increased 69%, from 5·2% in a population of 7, 908,562 in 1995 to 8·8% of 9,276,945 in 2005. Or said another way, the number of people with diabetes increased by 113%, from 388,433 to 827,419 during that time, whereas the population grew by only 17%.

2.    The prevalence rates increased to a greater extent in the population under age 50 (94%) than in those older than age 50 (63%). 

3.    The prevalence increased in a straight line by 6.2% annually from 1995 to 2005. 

4.    The overall prevalence remained higher in men than in women (9·4% vs 8·4% in 2005, p<0·0001).

5.    In 2003, the incidence rate was a shocking 8.2 per 1,000 people. 

6.    The greatest rise in diabetes occurred in young women (108·2%) compared with an 81·4% increase in young men.

7.    The rate of new cases increased 31% from 6·6 per 1000 in 1997 to 8·2 per 1000 in 2003.  Translated: more cases are being diagnosed today than ten years ago. 

8.    The adjusted mortality rate in people with diabetes fell by 25% from 1995 to 2005.  Of note, however, the mortality improvement in Ontario, Canada is higher (better) than that seen in other developed nations, notably Denmark and the US. 

Diabetes is one of the most costly “and burdensome” chronic diseases and a leading cause of blindness, vascular disease (myocardial infarction, stroke, and peripheral artery occlusion), and renal failure.   

This increase in type-2 diabetes can be directly attributed to increasing obesity rates; obesity in Ontario increased 20% to 30% during the study period.    The authors pointed out that the new diagnostic criteria did not appear to cause the increase in incidence. 

The obesity epidemic is a new phenomenon (see CDC obesity tables) , continues to grow, and cause an epidemic of type-2 diabetes and will shorten life expectancy.  We know that to be true for individuals with obesity and diabetes, but these population numbers appear powerful enough to impact future population life expectancy. 

This is a tsumani that we are poorly prepared to handle and one that will consume the health care system.  We must start to seriously attack this obesity epidemic.   Are you at risk? 

Stay well.  Live well as long as you are alive.  Dr. Bob

Selling life insurance policies and life expectancy

The market for stranger (or investor) owned life insurance and viatical settlements (selling an unwanted policy for cash) is a $15 billion dollar market--and growing.  As currently structured, this market is troublesome, the risks volatile, and the potential costs to the insured high. 

Misunderstanding life expectancy is a major risk for all parties of the transaction, except the broker who makes money by prospecting and completing the sale.   

A 75-year old, who was in good healthy with no major diseases, recently asked my professional opinion about selling his $2,000,000 policy with a cash value of $600,000.  My client was disputing the amount offered (the longer the life expectancy, the lower the price paid.) The broker indicated that the buyers were estimating his life expectancy at 14 years (or age 89), while, based on life expectancy articles in the newspaper, he thought that his own life expectancy was 8 years (or age 83).   

I told him, all other things being equal and given no other health problems, a 75-year old who smokes, or weighs 300 pounds, or has type-2 diabetes, or has  Parkinson's, or the early stages of Alzheimer's has an 8-year life expectancy.   

The moral of the story is that we are all going to live longer than we think, often much longer, even those of us who have a disease or medical condition.    Throw in two or three medical diseases or complications and the formula starts to change, but not until then. 

    

How do life insurers count?

Life insurers count very carefully.  The insurance actuaries meticulously count lives and deaths, premiums charged and premiums collected, dividends and death claims paid.  And then the actuaries tell the underwriters how each rating class and age performed compared to how they priced it.    

The underwriting measurement of deaths is very precise.  Actuaries and medical underwriters count deaths per 1000 people per year.  Start with deaths per 1000 of the general population, then define deaths per 1000 with better than the general population, then deaths per 1000 by increasing risk group (e.g., smokers or those who have had cancer or those with cancer)  (I'll write much more about the measurement of death rates by age in future posts.) 

Here is a simple example.  Take two groups, Group A and Group B, each with 1000 people, all age 35, and  each of whom buy a  $1,000,000 ten-year term policy for $1,200 per year.   Group A and Group B are identical except all 1000 people in Group A weigh 160 pounds and those in Group B weigh 230 pounds, a 70 pound difference.   Does the 70 pounds make a mortality difference and why does the insurer care? 

After ten years, the insurance company will have collected $12,000,000 from each group.  The mortality rate in Group A was exactly what the company expected, 1 death per 1000 people per year or 10 deaths after 10 years.  The insurer paid ten $1,000,000 death claims for a total payout of $10,000,000, leaving $2,000,000 to run the business and profits over 10 years. 

But the mortality rate in group B was 1.5 deaths per 1000 per year.  Not a big difference, you say?  Just watch.  After 10 years, Group B will have 15 deaths and the insurer will have paid 15 $1,000,000 death claims or $15,000,000.  But the assumed "standard" mortality of 1 per 1000 per year for this group and only collected $12,000,0000 in premiums.  They face a deficit of $3,0000,000.  Five extra deaths over ten years is a loss of $3,000,000.  Wow, these numbers add up fast.         

    

Why underwrite?

Is life insurance underwriting important? You bet underwriting is important. Just think about the "sub-prime mortgage mess," where underwriting standards were first lax and then non-existent. 

I would argue that good underwriting is more important in life insurance because of asymmetry of knowledge between the buyer and the company and the huge leverage  between the very low term premiums and the huge benefit.  For example, a $1,000,000 term policy only costs about $1,000 per month.   Any asymmetry of knowledge (the applicant knows of a risk that is not disclosed to the company), quickly moves the odds in favor of the applicant.   

At the same time, the insurance company is in the business of selling life insurance and often looks quite hard for favorable factors that will allow them to issue a case or improve the rating.  But once, underwritten and placed in force, that risk will remain for decades to come and can not be canceled except for non-payment or premiums.   

Physicians who practice insurance medicine

I have "practiced" insurance medicine for 27 years as Medical Director of Northwestern Mutual, the largest underwriter of individual life insurance in America and am a member of the American Academy of Insurance Medicine, and a Fellow of the American College of Physicians.  And, I am now in private insurance medicine consulting at drbobgleeson.com (click on "insurance medicine" in the left-hand column). 

A physician specializing in insurance medicine applies his or medical knowledge to underwriting or risk classification, or, in the case of life insurance, to making a best estimate of when a person is likely to die.   Maybe not that person, but when half, the average, of a group of 1000 people of the same sex and age and with the same medical condition is likely to die. 

The physician's medical knowledge is used to develop or write the underwriting standards or guidelines and then apply those to individual applicants.  When setting guidelines, they write the rules determining the estimated life expectancy of people  who have salutary risk cardiovascular risk factors and no underlying disease, those who smoke cigarettes, those who have a melanoma, a heart attack.  When they look at individual case of, for example, someone who had a heart attack, they ask how much of the myocardium (heart muscle) was damaged, what was the treatment, what meds are they on, how good is their compliance, and how thorough is the follow-up?    The answers can either increase or decrease the risk assessment.   

Is insurance medicine interesting? You bet.  Intellectually stimulating?  Absolutely.  Challenging?  Every day.