A Picture of Adverse Selection Derived From the Federal Register

Adverse selection in health insurance is the proclivity of insureds who accurately perceive themselves to be of higher than average risk to purchase insurance with greater frequency and, where possible, with higher benefits, than those who accurately perceive themselves to be of lower than average risk. Unchecked adverse selection can greatly restrict the amount of risk that can be transferred through an insurance market and cause a true loss to society. Insurers normally attempt to reduce adverse selection by “underwriting,” i.e. offering different contract terms based upon the insurer’s hopefully unbiased assessment of risk.  Settings in which such underwriting is impracticable or unlawful create a serious impediment to optimal risk transfer.

The Affordable Care Act (a/k/a Obamacare) creates a risk of severe adverse selection by prohibiting medical underwriting in the sale of health insurance.  One of the ways it attempts to palliate the contraction of the market that would otherwise occur is by subsidizing insurers in rough proportion to the riskiness of their insurance pool. Thus, although the high risk insured does not pay the insurer more for insurance than the low risk insured (except in limited ways for age, tobacco use and geographic location), the insurer ends up getting more for enrolling high risk insureds due to transfer payments made under the Risk Adjustment provisions codified in 18 U.S.C. § 18063. To establish this system, the government, among other things had to estimate the true “demographic risk” of individuals.  By demographic risk, one means risk of medical claims independent of “ICD-9 diagnosable” medical conditions that the individual may have. This demographic risk is revealed in part by the age and gender of the insured but also by their selection from amongst four (or five) levels of expected benefits known as “actuarial value.”  Persons purchasing policies with high actuarial value and thus having lower deductible and copay requirements, tend to be riskier than those purchasing policies with low actuarial value and thus having higher deductible and copay requirements.

The government data collected in this effort to implement Risk Adjustment under 18 U.S.C. § 18063 and placed in the Federal Register (78 F.R. 15409, 15422 (March 11, 2013)) gives us a rare opportunity to really see adverse selection in action not just as a matter of theory but as an empirical proposition.  The interactive element below shows this clearly.  It presents a graph showing for each gender and adult age level for which insurance through a private insurer is likely obtained, the relationship between the actuarial value of the plan selected and the risk factor posed. (Do not concern yourself with the units in which risk is measured).  What one can see is that there is a definite correlation for all ages and genders between the actuarial value of the plan selected and the risk factor of the individual. The line turns pink when females are selected for examination, blue when males are selected for examination. If you see a picture but no interactive elements, you need to download the free CDF player available here.
[WolframCDF source=”http://mathlaw.org/wp-content/uploads/2013/04/a-picture-of-adverse-selection.cdf” CDFwidth=”600″ CDFheight=”400″ altimage=”http://mathlaw.org/wp-content/uploads/2013/04/a-picture-of-adverse-selection1.png”]

We can also show the relationship in which risk factor appears on the x-axis and the actuarial value of the plan purchased appears on the y-axis.

[WolframCDF source=”http://mathlaw.org/wp-content/uploads/2013/04/a-picture-of-adverse-selection-2.cdf” CDFwidth=”600″ CDFheight=”500″ altimage=”http://mathlaw.org/wp-content/uploads/2013/04/a-picture-of-adverse-selection2.png”]

One of the many interesting features of this visualization is that the data is independent of the insured’s knowledge of disease.  Disease is  dealt with separately by the regulations implementing the Risk Adjustment provisions of the Affordable Care Act. It is also apparently independent of moral hazard — the proclivity of insureds with higher levels of coverage to more frequently incur events covered by the insurance policy. In this context, moral hazard would mean the proclivity of people with, say, platinum policies that have low cost sharing, to visit medical professionals more frequently and provide less resistance to proposed expensive medical procedures than people with, say, bronze policies. That tendency is addressed in the modeling embodied in the Risk Adjustment regulations, but is addressed as a separate “Induced Demand” factor. Thus, not only do we get a well researched estimate of the actual extent of adverse selection, but we get its effects disentangled from those of moral hazard — at least if the government has done it right and I am reading the document correctly.

The picture also gives rise to a question.  The Affordable Care Act permits insurers to price health insurance based on age. So, is provision of transfer payments that includes age in the mix “double counting”?  Why does government need to subsidize that for which insurers already are compensated? Is it an effort to address the fact that the statute constrains the extent to which age counts, limiting the pricing ratio to 3:1 from most expensive age level to least expensive age level?  I don’t know the answer to this question and welcome comments.

Visualizing Hierarchical Condition Codes

Visualizing HCC

The HCC Graph

The Affordable Care Act attempts the ambitious feat of inducing private insurers to sell individual health insurance when medical underwriting is prohibited.  Ordinarily, such a prohibition might give rise to a paralyzing fear of adverse selection and an effort to evade the prohibition. The insurer might, for example, engage in selective advertising or networks that lacked oncologists in an effort to bring healthier individuals to one’s own insurance company and leave the sickly to one’s competitors. The Affordable Care Act attempts to reduce the return on any such subterfuge by a system of transfer payments known as “Risk Adjustment” (42 U.S.C. § 18063). Under Risk Adjustment the net premium the insurer receives on an individual is based on part on the medical risk that individual poses. Adverse selection is not reduced in the typical way of medical classification conducted by the insurer and contract terms such as price being based on the results of the classification. Instead,Risk Adjustment requires that the insurer offer the same contract to all comers but that government in effect conduct the classification and transfer funds to those insurers that happen to take on high risk insureds while transferring funds away from those insurers that happen to take on low risk insureds.

Implementing such a system is an enormously complex matter, as the hundreds of pages of regulations and explanations contained in recent Federal Register entries can attest. See, e.g. 78 Fed. Reg. 15410-15541 (March 11, 2013). From the universe of potential medical conditions, one must create a mapping of projected claims costs. The Department of Health and Human Services (DHHS) has now attempted this mapping through something it calls Hierarchical Condition Codes. The idea is to look at the “ICD9” diagnostic codes typically given patients and map that to a coarser set of Condition Codes that supposedly have roughly similar treatment costs.  But how does one handle the patient with multiple related ICD9 diagnoses?  One could develop a multivariable model that attempts to show a cost factor for every combination of Condition Codes. Such a model would likely be mathematically intractable, however. Instead, the idea is to say that there is a cost hierarchy of Condition Codes and to map certain subsets of Condition Codes into the Condition Code that generates the highest medical costs.  One then attaches some intensity coefficient to each potentially “upcoded” Condition Code. This system, which has apparently been used before in the Medicare program, is known as Hierarchical Condition Codes.

Mathematically, the Department of Health and Human Services has created a graph. The nodes of the graph are the union of the set of ICD9 codes and the set of HHS Condition Codes.  The edges of the graph are the mapping between ICD9 code and HHS Condition Code and the mapping between subsumed HHS Condition Codes and its “basin of attraction”: the Condition Code to which these subsumed codes are remapped.

It turns out that not only has DHHS created a conceptual mathematical object, it has provided the data from which such an object can be visualized. It is contained in an Excel spreadsheet available at http://cciio.cms.gov/resources/files/ra_tables_proposed_1_2013.xlsx. I now show how one can use this data to produce a visualizing of the HCC Graph.  To see this, you will need the free CDF player available here. If all you see here is a picture, you don’t have the Player. Also, a warning. This is a large CDF file.  It may take a minute or so for it to load into WordPress, with the precise time depending on your computer and your connection speed. Be patient.

[WolframCDF source=”http://mathlaw.org/wp-content/uploads/2013/04/Visualizing-HCC.cdf” CDFwidth=”700″ CDFheight=”8400″ altimage=”http://mathlaw.org/wp-content/uploads/2013/04/Visualizing-HCC.png”]

To be honest, I am not quite sure what this all proves. It confirms, I believe, the enormous complexity of the enterprise of the Affordable Care Act.  To make health insurance purchases less sensitive to the fortunes of health but to preserve in at least name the idea that this is not a government takeover of health insurance, government permits private insurers to sell policies but prohibits the normally necessary step of medical underwriting.  But to prevent insurers from simply abandoning a system in which adverse selection might ordinarily cause a death spiral, government then undertakes its own surrogate classification system and pays insurers whose pool is drawn from the more expensive patients. But doing this requires huge amounts of data collection, a system of recoding, and then a system that computes a transfer payment based on this and much more information.  It remains to be seen whether this system succeeds in keeping private insurers involved in healthcare finance.  In the mean time, however, it does produce what I regard as some attractive pictures.