My Iowa post last week got a whole lot more attention than I thought it would have and provoked several great conversations. One of the questions was about how to deal with this extreme corner case and if a high cost risk pool made sense for this use case. This is the perfect example for a high cost pool.
And then there was a great discussion on how to design such a program. And this leads to a discussion about clustering. Bigger risk pools are more efficient and effective at spreading risks, so we’re going to talk about clustering for a bit.
@onceuponA @matthewherper @afrakt @aaronecarroll @larry_levitt @ASlavitt @LorenAdler Ideally national pooling not 51 state pools as random clustering would be ugly for small state much less non-random cluster of doom
— David Anderson (@bjdickmayhew) April 21, 2017
Let’s imagine a hypothetical high cost risk pool of the top 1,000 individual claim years in the country. The average claim will be $5,000,000 for the year. Let’s simplify things and say 990 are randomly distributed by population and 10 are a non-random cluster that we can insert into any state at any time. The first run through is with fifty one state (and DC) based high cost risk pools. We’ll look at two states, California and Wyoming, for this run.
California has about 10% of the population. California should expect to see 99 people in this hypothetical pool plus an expectation that one of the ten non-random people would be expected to be in California. Their expected high cost risk pool budget is $500 million. Now if all ten of the non-randomly clustered people are in California, they increase the expected pool costs by 9%. California is big enough and rich enough that a surprise $45 million dollar medical expense does not destroy their budget.
Now Wyoming should expect to see between 1 and 2 people qualify for the high cost risk pool. Let’s assume the Wyoming state government is very cautious and they allocate $10 million for the high cost risk pool. That works great in a normal year. But if the travelling roadshow of catastrophic medical expenses arrive in Cheyenne, the state is now on the hook for twelve qualified individuals. They are 500% over budget now and the state budget is underwater.
This thought experiment is amazingly unrealistic.
Even if we are to assume that extreme medical cost cases are randomly distributed, we should expect several states to be surprised at the number and expense that they face as Pennylsvania could reasonably expect to see anywhere from 45 to 51 qualifying individuals from the scenario above in any given year just do to random chance.
More importantly, we know that diseases are not randomly distributed. My ongoing freak-out about Zika is based on the fact that this is a concentration of very high need and high cost individuals on states with low Medicaid funding. Genetic disorders are tightly clustered due to both the combination of most people live near their families rather than being randomly distributed and localized clusters of diseases have led to local medical-industrial clusters of medical knowledge and treatment. For instance, maple syrup urine disease is a common genetic disorder among Amish families, so there is a good deal of knowledge on treating that disease clustered in Lancaster County, Pennyslvania and Holmes County, Ohio. Sickle cell disorders are overwhelmingly a disease of African Americans, so it is more common in Mississippi than Montana.
From a financial perspective, there is a chance that there is enough sample size that although one state will have more of one genetic disorder it washes out as another disorder it is light in is dis-proportionally prevalent in another state so the cash flows balance out. That is an empirical question that I don’t know enough to answer. But even if genetic disorders balance out, localized outbreaks like Zika won’t balance out.
State based high cost risk pools would remove some of the falling knife incentives that I described in Iowa but they will be underfunded and overwhelmed at times of high need. National level pooling is far more efficient and effective.