Lapse-supported life insurance and adverse selection

Should life insurers have access to genetic test results?

Long-awaited progress in addressing genetic discrimination in the United States

Genetic testing is a concern for insurers if they cannot use test results in underwriting. We model adverse selection in an insurance market with genetic testing for breast and ovarian cancer. Increased forces of mortality resulting from a family history of cancer or a positive test for a BRCA mutation are calculated. Using a Markov model, we estimate costs of adverse selection, assuming various testing and insurance purchase behaviors. Adverse selection should be controllable if companies apply strict underwriting rules, requesting cancer history and onset age for all first-degree relatives. If insurers fail to correctly identify the family history of the application and use it in pricing, adverse selection costs could become unbearable. GENETIC TESTING AND THE FEAR OF ADVERSE SELECTION Adverse selection can be defined as the process by which prospective policyholders may gain financial advantage through insurance purchase decisions based on risk characteristics known to them, but unknown and not revealed to the insurer. It is a source of concern for insurance companies because it could result in underpricing. Recent developments in the Human Genome Project, while offering medical promise, have further increased insurer fears about this issue. Indeed, in many jurisdictions, insurers are not allowed to ask for results of genetic testing. Several years ago, two gene mutations that affect the likelihood of developing breast and ovarian cancer were discovered. Commercial tests to detect the presence of these mutations are now available. Women who learn through genetic tests that they are at higher risk of death for breast or ovarian cancer may purchase more life insurance, which to them looks inexpensive since it is priced at rates set for average risks. Women who learn they are at lower risk after a negative test may purchase less life insurance. These two forces combine to increase the aggregate mortality of the insurance purchasers. If insurers do not have access to the test results, they are unable to identify which women are at higher risk and which are not. They have to increase premiums for everyone, driving those at lower risk out of the pool. This creates a spiral of increasing prices and a decreasing number of policies issued, which may threaten the financial solvency of the insurer. The debate about insurer access to genetic screening information has industry representatives pointing to the risk of adverse selection. They advocate mandates that would require that all test results provided to individuals also be made available to insurers. These insurers' request for a level playing field contrasts with efforts by consumer groups to increase the privacy protection of genetic information. Consumers are concerned that test information may find its way to employers or result in employment and social discrimination. They fear that the use of genetic testing by insurers could result in the creation of a biological underclass of uninsurable individuals. The issue is highly emotional and very political. While risk classification is unchallenged in some lines of business (smokers/nonsmokers in life insurance, distance from nearest fire station in homeowners' insurance), the use of genetic tests comes into conflict with social and moral concerns of society in the 1990s. These concerns have prompted legislators to regulate the use of genetic testing. Wisconsin was the first state to introduce a genetic testing law in 1992. Thirty-four states have now enacted laws prohibiting insurers of different types from using genetic information in their underwriting decisions. As of early 1998. more than 200 bills have been proposed in various state legislatures throughout the country that try to limit insurers' access to and use of genetic information (Jones, 1999). In March 1999, Sen. Olympia Snowe (R-ME) introduced a bill, backed by President Clinton and included in the GOP Patients' Bill of Rights, that would block insurers from denying coverage or setting premiums base d on genetic information or family history (Government Relations Weekly Update, 1999). …

The dramatic increase in genetic testing for adult-onset diseases has created a debate regarding whether or not insurance companies should be able to use genetic test results in underwriting. We use data from women who have been tested for the BRCA1 gene mutation along with data from otherwise comparable untested women to assess the potential for adverse selection in the life insurance market when tested individuals know their genetic test results but insurers do not. Our analyses show that women who test positive for the BRCA1 gene mutation do not capitalize on their informational advantage by purchasing more life insurance than those women who have not undergone genetic testing.

The Human Genetics Commission intends to study the use of family history in underwriting, regarding it as genetic information in a broad sense. We survey the developments in genetics and insurance that led up to this decision. We propose a model for studying the possible costs of adverse selection in the mortgage-related life insurance market (which is important in the United Kingdom) with and without moratoria on the use of family history. We consider both level and decreasing term assurances, finding that the cost of adverse selection is slightly higher for the latter. Several assumptions about mutation penetrance, incidence of additional mortality, prevalence of genetic testing and insurance-buying behaviour are considered. Overall we conclude that: (a) a moratorium on genetic test results alone will lead to negligible adverse selection costs; while (b) a moratorium extended to family history will lead to premium increases just by redefining underwriting classes, with adverse selection being possible as well, but only in extreme circumstances would all premium increases be likely to exceed about 10%. However, we give examples in a model of a smaller life insurance market that show that our benign conclusions might not apply more generally.

Genetic test results do not affect the demand for life insurance

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Life insurance industry access to genetic information is controversial. Consumer groups argue that access will increase discrimination in life insurance premiums and discourage individuals from undergoing genetic testing that may provide health benefits. Conversely, life insurers argue that without access to risk information available to individuals, they face substantial financial risk from adverse selection. Given this controversy, we conducted a retrospective cohort study to evaluate the impact of breast cancer risk information on life insurance purchasing, the impact of concerns about life insurance discrimination on use of BRCA1/2 testing, and the incidence of life insurance discrimination following participation in breast cancer risk assessment and BRCA1/2 testing. Study participants were 636 women who participated in genetic counseling and/or genetic testing at a University based clinic offering breast cancer risk assessment, genetic counseling, and BRCA1/2 testing between January 1995 and May 2000. Twenty-seven women (4%) had increased and six (1%) had decreased their life insurance since participation in breast cancer risk assessment. The decision to increase life insurance coverage was associated with predicted breast cancer risk (adjusted OR 1.03 for each 1% absolute increase in risk, 95% CI 1.01-1.10) and being found to carry a mutation in BRCA1/2 (OR 5.10, 95% CI 1.90-13.66). Concern about life insurance discrimination was inversely associated with the decision to undergo BRCA1/2 testing (RR 0.67, 95% CI 0.52-0.85). No respondent reported having life insurance denied or canceled. In this cohort of women, these results indicate that information about increased breast cancer risk is associated with increase in life insurance purchasing, raising the possibility of adverse selection. Although fear of insurance discrimination is associated with the decision not to undergo BRCA1/2 testing, there was no evidence of actual insurance discrimination from BRCA1/2 testing.

Most studies of life insurance selection focus exclusively on mortality risk, producing conflicting results. We introduce lapse behavior as a critical second channel for selection. Theoretically, we show that both adverse and advantageous selection can coexist within the same market, depending on the relative strength of opposing effects from income, mortality risk, and lapse probabilities. Empirically, we validate this using administrative data from U.S. life insurance companies' statutory filings (1993-2017). Our results confirm strong adverse selection: a 1% increase in premiums leads to a 0.13% increase in insurers' future costs. This selection is driven entirely by lapse behavior--higher premiums reduce future lapse rates by 0.145%--while mortality rates show no effect. Our demand elasticity estimate of -0.9 reveals significant market frictions. We analyze three potential policy interventions--individual-base lapse pricing, contemporaneous risk-based premiums, and actuarially consistent cash surrender values--each offering different trade-offs for market efficiency and consumer welfare.

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We analyse the effects of regulations prohibiting the use of information to risk-rate premiums in a life insurance market. New information derived from genetic tests is likely to become increasingly relevant in the future. Many governments prohibit the use of this information, thereby generatingregulatory adverse selection� . In our model, individuals early in their lives know neither their desired level of life insurance later in life nor their mortality risk, but learn both over time. We obtain both positive and normative results that differ qualitatively from those in standard, static models. Legislation prohibiting the use of genetic tests for ratemaking may increase welfare. The effort to sequence the human genome completely is now effectively com- pleted. Although a comprehensive understanding of the effects of most genes on health and mortality risks will still take substantial further effort, it is likely that in the medium-term future more and more information relevant to ratemaking in the life, health, and long-term care insurance markets will become available. In particular, we will learn the effects of many genes on the likelihood of various illnesses and consequently individualslife expectancy. 1 Moreover, new genetic tests for these genes will likely become much cheaper. 2 Allowing this information to be used by insurers raises the prospect of substantial changes in future pricing

Background and Aims In Europe, the share of the elderly (≥65 years of age) in the total population is estimated to increase from 19.2% in 2016 to 29.1% by 2080. In 2016, European Renal Best Practice (ERBP) group published a clinical practice guideline on management of older patients with CKD stage3b or higher (eGFR<45ml/min/1.73 m2). Two risk stratifications scores were emphasized: Bansal score for prognosticating risk of death in medium term, and Kidney Failure Risk Equation (KFRE) for estimating progression of CKD stage 3b or 4 to ESRD. Our group, as part of the ERBP team, aimed to evaluate and apply the framework proposed by the guideline, consisting of risk prediction for both mortality and progression to ESRD in a cohort of elderly patients with advanced CKD. After dividing the population in groups of risk, we described their real-life trajectory in terms of either reaching ESRD/death. Method In this retrospective cohort study we included patients aged ≥65 years with CKD stage 3b-4, evaluated at the Outpatient Nephrology Department of Dr. C. I. Parhon Hospital from Iași, Romania, between October 2016 – October 2018. Individual risk for mortality was predicted using Bansal score, a nine-variable equation model. A total score of 7 (associated with a mortality risk of 53.82%) was established as cut-off value to differentiate between 2 groups: high risk of mortality (Bansal ≥ 7) and low risk of mortality (Bansal < 7), given the fact that the ERBP guidelines don’t define a threshold for high risk in respect to mortality outcome. According to the algorithm proposed by the guideline, individual risk for progression to ESRD at 5 years was calculated in the low mortality risk group, using the 4-variable Kidney Failure Risk Equation (KFRE). Results The final cohort included 958 patients, with a mean age of 74 years (SD: 7), and with similar gender distribution (50.6% female vs. 49.4% male). Predicted trajectory in terms of reaching ESRD / death: When we applied Bansal score for mortality, the total study population (N=958) was divided in two groups: N1 with high risk of mortality, which comprised more than half of the cohort (548 patients, 57.2%) and N2 with low risk of mortality (410 patients, 42.8%). Individual risk of progression to ESRD was then estimated in N2 group, using 4-variable KFRE. Nearly ¾ of this group (75.4%, 309 subjects) presented a low-risk of progression and ¼ (24.6%, 101 subjects) had high-risk. Real-life trajectory in terms of reaching ESRD / death: From the entire cohort, 31 patients started renal replacement therapy (RRT) and 164 patients died as their first clinical event. The RRT initiation rate was 3.6% of N1 group (20 subjects) versus 2.7% of N2 group (11 subjects). The mortality rate was 15.5% of N1 group (85 deaths) versus 19.3% of N2 group (79 deaths). Figure 1 depicts the real-life trajectory of the population groups in terms of reaching ESRD / death. Conclusion In a large population from Eastern Europe, the application of the algorithm from the Clinical Practice Guideline on management of older patients with advanced CKD showed that risk prediction for death and end-stage renal disease does not parallel the real-life trajectory of the population. More than half of the subjects had a high risk of mortality, however we found similar death rates in the 2 groups (high versus low risk of mortality). Also, the RRT initiation rates were similar, irrespective of predicted mortality risk or kidney failure risk, suggesting that implementing the guideline in real-life settings is still a challenge.

The decreasing cost of genome and exome sequencing, including direct-to-consumer testing, produces information asymmetry between applicants and life insurance companies, which increases the risk of adverse selection. Rather than requiring applicants for life insurance to undergo genetic testing, life insurers ought to revise their policies and not use the results of genetic tests in the medical records of applicants for underwriting. Prohibitions on access to genetic test results already have been adopted without great upheaval in several other countries. There are two main reasons why this new approach should be used in the United States. First, genetic test results do not provide essential information that is not already available through traditional underwriting measures. Second, because many individuals at genetically increased risk of serious disorders currently decline testing out of fear of the economic consequences, policies prohibiting the use of genetic test results in life insurance will encourage genetic testing and facilitate timely surveillance and necessary medical intervention, thereby saving lives.

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