I saw this going viral, “How GLP-1s Are Breaking Life Insurance.”
Typically, underwriters- suspiciously sounds like undertakers-rely on a handful of key health metrics like HbA1c, cholesterol, blood pressure, and BMI to calculate your risk of dying earlier than expected (and thus costing them money).
Those eagle-eyed readers among you have probably noticed something interesting already. Those same four metrics are exactly what GLP‑1s improve. Not just a little, but enough to entirely shift someone’s risk profile within at least 6 months of using them.
The author greatly overstates the problem.
Rather, the underreporting of obesity is a much bigger problem for health insurers compared to life insurers. This is because health insurance can be thought of as a path dependency problem, whereas life insurance is a ‘first arrival’ problem. With life insurance, obviously the underwriter only pays out when the policy holder dies. Simple enough. But with health insurance, each time there is a medical event means a payment. This can become much more expensive as there is much more variance of payouts, made worse by obesity. It’s easier to predict how long someone will live compared to how many medical expenses they will incur.
The article claims, “Life insurers can predict when you’ll die with about 98% accuracy.” There was some confusion in the comments as to what this means. A link to a dense 21-page PDF does not clarify the matter. (It would have been more helpful had he actually cited the relevant passage, but most people will just take his word for it than read the whole thing). I interpret it to mean that underwriters can write a policy in which they turn a profit 98% of the time. There are well-established, empirically-sound formulas that can predict life expectancy. But medical expenses are much more unpredictable, as many people can attest, like getting into a car accident or a sudden illness.
Second, GLP-1 drugs do not change the calculations of life insurance policies by much. This is because GLP-1 drugs do not reduce mortality much, or even at all. As I explain in the post “‘Universal Ozempic’ would bankrupt America’s healthcare system for little benefit”, except for severe obesity (40-45+ BMI, which is really fat) life expectancy is not lowered much in men and even less in women in the setting of obesity. I’m sure we have all seen old obese people at Walmart or wherever, yet we all know thin people who have died prematurely due to accidents or cancer. Medical technology has also gotten really good at keeping obese people who have chronic illnesses alive for a long time, such as diabetes or chronic heart disease.
This is why Medicare hasn’t yet covered these drugs, because the ROI is not worth it. (Although when these drugs go generic, the math will become much more favorable.) Instead, these drugs are more cosmetic or about improving quality of life, than lifesaving, except for severe obesity, >45+ BMI, which is only a small fraction of obese people. It’s just that being obese makes all sorts of health indicators worse, yet people do not die much sooner. Having diabetes, difficulty walking up stairs or having to buy extra airline seats does not mean dying much sooner.
For example, according to Aflac, a “5’3″, 30-year-old woman purchasing a 20-year, $100,000 term policy” will pay $11/month at 130lbs, but this only increases to $15/month at 232lbs, which represents an increase of BMI from 23 to 41. In fact, only at 280lbs(!) is it deemed uninsurable. Or to put it another way, someone who is morbidly obese can still buy life insurance for about the same cost as an annual Netflix subscription. Remarkable, but that is what the data says, anyway.
By comparison, smoking corresponds to a 200% increase of premiums, being that it’s much more deadly. Americans have replaced one addiction, smoking, with calorie-dense food, which although not as deadly, leads to obesity. One can make the case that it’s rational to eat junk food if it provides the same dopamine hit as smoking, but is much safer.
Moreover, insurers can easily adapt to this by adjusting the premiums accordingly to account for GLP-1 users, or by requiring periodic exams and then adjusting the premiums accordingly depending on the terms of the policy. This is what actuaries do for a living. They use this data to recalculate premiums for new policy holders. Following the recent California fires, for example, State Farm and Allstate responded by either denying coverage or raising premiums. It’s already assumed that a certain percentage of people will lie or otherwise misrepresent their health status on their application, and this cost is shifted to honest policy holders who end up paying more.