Genetic Prediction and Adverse Selection

This paper asks how much adverse selection would arise in critical illness insurance (CII) markets if consumers can observe polygenic indexes (PGIs) — genetic risk scores derived from millions of genetic variants — while insurers are legally barred from using genetic information. The authors develop an econometric method that measures selection under current PGI technology, then extends identification to expected future PGI accuracy using heritability bounds, even though future PGIs are not yet observable in data.

The primary dataset is the UK Biobank (UKB), comprising approximately 446,570 genotyped individuals of European-like ancestry linked to NHS electronic health records. The authors study seven single-disease CII contracts (Alzheimer’s disease, breast cancer, coronary artery disease, colorectal cancer, prostate cancer, schizophrenia, and type 2 diabetes) and multiple-disease bundled contracts paying a lump sum upon onset. The econometric model assumes a probit disease probability, Gaussian PGI structure, and identification relies on published heritability estimates to pin down future PGI predictive power. The key selection metric is the implicit tax proposed by Hendren (2013): the percentage markup a marginal consumer must pay above her actuarially fair price due to adverse selection. The authors use the minimum implicit tax up to the 80th percentile of risk (t80) as their summary statistic, with market unraveling benchmarked at t80 between 43% and 83% from prior literature.

The paper reports three main findings, all scoped to a population of 35-year-olds in the standard insurer risk class (those whose predicted risk falls within 0.75–1.25 times the population mean).

First, under current PGI technology with full consumer adoption, selection is noticeable but heterogeneous across diseases. t80 ranges from 17.9% for coronary artery disease to 117.9% for Alzheimer’s disease. Coronary artery disease and colorectal cancer fall in the middle of the no-unraveling range; breast cancer, schizophrenia, and type 2 diabetes fall between the no-unraveling and unraveling ranges; Alzheimer’s disease and prostate cancer (t80 = 59.8%) reach or exceed the unraveling range. The current prostate cancer PGI explains 9.9% of liability variance, adding 8.3 percentage points over the 22.9% explained by non-genetic covariates.

Second, under expected future PGI accuracy — bounded below by SNP heritability and above by twin heritability — selection becomes potentially crippling. Under the lower bound (Scenario 3L), t80 ranges from 57.5% for breast cancer to above 1,000% for Alzheimer’s. Under the upper bound (Scenario 3U), t80 exceeds 100% for all seven single-disease contracts and exceeds 1,000% for three of them. For prostate cancer, the reference case, t80 reaches 86.8% under Scenario 3L and 426.9% under Scenario 3U — far above Hendren’s unraveling benchmarks. For multiple-disease male contracts, t80 = 30.8% under current technology, rising to 54.4% (Scenario 3L) and 243.9% (Scenario 3U).

Third, variation in selection across contracts is driven primarily by: the predictive power of the future PGI, the incremental predictive power over non-genetic covariates, and disease prevalence. Alzheimer’s and schizophrenia — high heritability, low prevalence — display the highest implicit taxes; breast and colorectal cancer — lower SNP heritability, lower incremental R2 — display the lowest.

These findings are corroborated by a calibrated Akerlof-Einav-Finkelstein equilibrium model using HRS data: current PGI availability reduces equilibrium market quantity from 30% to 21.4%; future PGI availability drives equilibrium quantity to zero in a full adverse selection death spiral. Partial take-up robustness checks show that even at 50% consumer adoption, selection remains problematically high under future PGI accuracy for most contracts. The analysis is restricted to individuals of European-like ancestry due to data availability constraints.

Q: What is the core market failure the paper analyzes? A: The paper analyzes adverse selection arising from an asymmetric information gap: consumers can observe PGI-based disease risk predictions from consumer genetic tests (e.g., 23andMe), while insurers in many jurisdictions are legally prohibited from requesting or using genetic information. This creates a situation where high-risk consumers have private information allowing them to sort into insurance, driving up average claims costs and potentially unraveling the market.

Q: What is a polygenic index (PGI) and why does it differ from classical genetic testing? A: A PGI is a weighted sum of millions of genetic variants (typically over one million) each with individually tiny effects, constructed using effect-size estimates from genome-wide association studies (GWASs). This contrasts with traditional genetic testing focused on rare single-gene mutations (e.g., BRCA for breast cancer or PKD for kidney disease), which are rare, explain small shares of population-level disease variance, and can largely be inferred from family history. PGIs target common polygenic diseases and are the primary driver of the adverse selection concern because they aggregate diffuse genetic signals into a meaningful risk prediction.

Q: What are the current PGI R2 values for the seven diseases studied? A: Estimated on the liability scale in the UKB, current PGI R2 values are: Alzheimer’s disease 7.1%, breast cancer 6.7%, coronary artery disease 2.5%, colorectal cancer 2.2%, prostate cancer 9.9%, schizophrenia 4.9%, and type 2 diabetes 7.4%. These represent the share of liability variance explained by each disease’s current PGI in the study sample.

Q: How does the paper identify the degree of selection under future PGI technology that does not yet exist in the data? A: The identification strategy combines three elements: the normality of PGI distributions, the relationship between current and future PGIs (the current PGI is modeled as a noisy version of the future PGI with an independent Gaussian error), and published heritability estimates that bound the future PGI’s predictive power. Theorem 1 establishes that under five stated assumptions — including a probit disease model and known future R2 from heritability studies — the full joint distribution of loss, current PGI, future PGI, and non-genetic covariates is identified from observed data.

Q: What heritability bounds are used for the future PGI scenarios, and why two bounds? A: Scenario 3L sets future PGI R2 equal to each disease’s SNP heritability (estimated from common genetic variants), which the authors treat as a conservative lower bound because future PGIs will also incorporate rarer variants with better effect-size precision. Scenario 3U sets future PGI R2 equal to twin heritability, treating it as an upper bound since the theoretical maximum predictive power of a PGI is the trait’s narrow-sense heritability. For prostate cancer, these bounds are 18.0% (SNP) and 57.0% (twin); for Alzheimer’s, SNP heritability is 33.1% and twin heritability is 58%.

Q: What is the implicit tax and how is it used as a benchmark? A: The implicit tax t(r) for a consumer with private risk r equals the percentage by which her insurance cost exceeds her own actuarially fair price when she must pool with all consumers of equal or higher risk. It measures how much the marginal buyer overpays due to adverse selection. The authors follow Hendren (2013) in reporting t80, the minimum implicit tax up to the 80th percentile. Hendren’s benchmarks: t80 between 7–35% for markets that did not unravel; t80 between 43–83% for markets that had unraveled.

Q: What are the single-disease contract results under current PGI technology (Scenario 2)? A: With full consumer adoption of current PGI technology, t80 ranges from 17.9% for coronary artery disease to 117.9% for Alzheimer’s disease. Coronary artery disease (17.9%) and colorectal cancer (26.5%) fall in the middle of Hendren’s no-unraveling range. Breast cancer (36.9%), schizophrenia (42.1%), and type 2 diabetes (37.0%) fall between the no-unraveling and unraveling ranges. Alzheimer’s disease (117.9%) and prostate cancer (59.8%) reach or exceed the unraveling range.

Q: What are the single-disease contract results under future PGI technology? A: Under the lower bound (Scenario 3L, R2 = SNP heritability), t80 ranges from 57.5% for breast cancer to above 1,000% for Alzheimer’s disease. Under the upper bound (Scenario 3U, R2 = twin heritability), t80 exceeds 100% for all seven contracts and exceeds 1,000% for three (Alzheimer’s, schizophrenia, and at least one other). These figures substantially exceed Hendren’s unraveled-market benchmarks for virtually all contracts.

Q: What drives cross-disease variation in the implicit tax? A: The authors identify three main drivers: the expected accuracy of future PGI (higher heritability → higher implicit tax), the incremental predictive power of the future PGI over non-genetic covariates observable by insurers (more incremental information → more adverse selection), and disease prevalence (lower prevalence concentrates risk heterogeneity, amplifying selection). Alzheimer’s disease and schizophrenia — high heritability and low prevalence — have the highest implicit taxes. Breast and colorectal cancers — lower SNP heritability and lower incremental R2 — have the lowest.

Q: What do the multiple-disease bundled contract results show? A: For the male multiple-disease contract under Scenario 2 (current PGI), t80 = 30.8%, comparable to Hendren’s no-unraveling range. Under Scenario 3L, t80 = 54.4%; under Scenario 3U, t80 = 243.9%, both in or above the unraveling range. The female contract yields qualitatively similar results. Implicit taxes in bundled contracts are generally lower than in single-disease contracts, suggesting some diversification of genetic risk across diseases.

Q: What does the calibrated equilibrium model find? A: Using an Akerlof (1970) / Einav-Finkelstein-Cullen (2010) supply-and-demand model calibrated to match a 30% market participation rate and a 50% loss ratio in the UK CII market, and using HRS data on individual risk aversion, the model finds that current PGI availability reduces equilibrium quantity from 30% to 21.4%. Future PGI availability (both Scenario 3L and 3U) drives equilibrium quantity to zero — a complete adverse selection death spiral with no trade.

Q: How robust are results to partial consumer adoption of genetic testing? A: At 10% consumer take-up, selection is low regardless of PGI accuracy. At 50% take-up, selection remains problematically high for all single-disease contracts under future PGI accuracy (Scenarios 3L and 3U). For multiple-disease contracts at 50% take-up, t80 falls just below Hendren’s unraveling threshold under Scenario 3L but enters the unraveling range under Scenario 3U. This suggests market problems would materialize once predictive power exceeds the SNP heritability bound and take-up exceeds roughly 50%.

Q: What role do risk preferences play, and do they confound the results? A: The authors test whether risk tolerance correlates with disease risk in the UKB using a self-reported general risk tolerance measure. They find extremely low correlations between risk tolerance and each disease. This is consistent with low correlation between relative risk aversion and disease risk in the HRS calibration, and supports the finding that correlation between risk and risk preferences is unlikely to meaningfully affect the main results.

Q: What is the paper’s assessment of preventive treatment as a mitigating factor? A: The authors acknowledge that genetic testing could enable personalized preventive medicine, which would reduce actual disease incidence among high-risk individuals. However, they argue this is unlikely to substantially affect their main findings because the most commonly covered diseases under CII are cancers, for which preventive behaviors have bounded effectiveness.

Q: What are the paper’s policy implications? A: The paper situates the genetic information problem within the standard regulatory framework for selection markets, distinguishing laissez-faire (allow genetic underwriting — efficient but potentially unfair to high-risk consumers), government provision (unattractive for non-essential CII), and managed competition (community rating combined with subsidies and risk adjustment). The authors argue that a full ban on genetic underwriting — the current policy in many countries — may become untenable as PGI accuracy improves, because it generates potentially crippling adverse selection. Some level of community rating may remain desirable for redistribution, but needs to be paired with subsidies or risk adjustment to prevent market collapse.

Q: What are the main data and scope limitations? A: The analysis is restricted to individuals of European-like ancestry because most large GWASs were conducted in European ancestry samples and PGIs perform poorly across ancestries. The UKB sample was aged 40–69 at recruitment and the analysis adjusts for age-dependent covariates; the HRS replication uses approximately 20,000 individuals. The equilibrium model ignores moral hazard and uses a parsimonious binary loss framework. The paper does not specify a timeline for when PGI accuracy will reach heritability bounds.

Polygenic Index (PGI): A weighted sum of an individual’s genetic variants across the genome (typically over one million variants), constructed using effect-size estimates from a genome-wide association study (GWAS) conducted in an independent sample. It is a noisy proxy for the individual’s true additive genetic factor for a disease, and its predictive power is bounded above by the trait’s narrow-sense heritability.

Implicit Tax: A measure of adverse selection defined by Hendren (2013) as the percentage by which a consumer with private risk r must overpay relative to her own actuarially fair price if she is pooled with all consumers of equal or higher risk. The minimum implicit tax up to the 80th percentile of risk (t80) serves as the paper’s primary summary statistic; t80 above roughly 43% is associated with market unraveling in prior literature.

SNP Heritability: The share of variance in a disease’s liability attributable to the set of common genetic variants (SNPs) used in heritability estimation. Used in this paper as a conservative lower bound on the predictive power of future PGIs, because future PGIs will additionally capture rarer variants.

Twin Heritability: An estimate of a trait’s narrow-sense (additive) heritability computed by comparing resemblance of monozygotic twins (sharing 100% of their genomes) to dizygotic twins (sharing ~50% on average). Used as an upper bound on future PGI predictive power, since heritability is the theoretical maximum R2 for a PGI.

Standard Risk Class: The set of consumers whose predicted disease risk (based on non-genetic covariates observable to insurers) falls between 0.75 and 1.25 times the population-wide average risk, following standard insurance underwriting practice. Insurers charge the same premium to all consumers in this class; any variation in risk within the class due to private genetic information constitutes the source of adverse selection analyzed in this paper.

Private Risk Function: The probability rho(g, w) of contracting the disease conditional on both the consumer’s observed PGI g and non-genetic factors w. Contrasted with the non-genetic private risk function pi(w), which conditions only on non-genetic covariates. The dispersion of the private risk distribution across consumers in the same risk class determines the degree of adverse selection.

Adverse Selection Death Spiral: The Akerlof (1970) mechanism in which high-risk consumers disproportionately purchase insurance, causing insurers to raise premiums, which deters low-risk consumers, which further raises the average risk of purchasers, ultimately driving equilibrium quantity to zero. The paper’s calibrated equilibrium model finds this outcome under future PGI accuracy for the HRS CAD contract.