RAND Workforce Composition

All Staff: 2017

all-staff-chartMale49%Female51%74%3%0%5%7%10%AsianBlackLatinxNative American*Two or More RacesWhiteRACE/ETHNICITYGENDER


Table 1. Outline of Framework for the Evaluation of Genetic Tests

Factors Relevant to Framework Area Description of the Factors Relevance to the Framework and the Insurance Industry
How useful is the test for characterising the risk of developing a condition?
Clinical utility Extent to which clinically relevant action can be taken based on the results of the test. For a test to have clinical utility, it must have demonstrated analytic validity, and scientific and clinical validity.

For tests available through the NHS, adoption in clinical practice is a proxy for clinical utility.

However, assessing this link for tests provided by direct-to-consumer (DTC) companies may be more difficult as they may not be equivalent to those used by the NHS.

Alternative information sources Extent to which predisposition to a given condition can be estimated using information other than genetic test results (e.g. family history or lifestyle). If information from a genetic test provides a more accurate estimate of disease risk than these alternatives, or can improve risk estimation when combined with them, there is risk of information asymmetry for insurers.
How many people take the test?
Societal acceptability Community's desire for genetic tests, which is influenced by whether the community benefits from the tests, as well as personal preferences and autonomy. Community in this context could be the general population, or those who are already at elevated risk due to family history or other factors. Interest in genetic testing varies by age, education, knowledge of genetics, family history of genetic conditions and the integration of genetic tests into the healthcare system. Consideration of uptake in certain subgroups may be important for insurers if the subgroup is more likely to have insurance or more likely to be at risk of developing a condition.
Personal utility Value of the information to the person being tested. Personal utility of a genetic test will vary by person and by the characteristics of the condition being tested for. Personal utility of a genetic test is likely to increase as capacity of genetic tests to estimate risk improves and/or as the range and effectiveness of interventions for a condition increase.
Availability of the test and clinical support for it How a test is accessed in terms of public (or private) medical system or DTC provision, eligibility criteria and the degree of clinical support both before and after testing. Under current NHS guidance, most individuals will only be referred for a genetic test if they are suspected by a clinician of having a certain condition, either due to symptoms or family history. Genetic testing for many conditions in the absence of family history or other indicative factors and without interaction with healthcare providers is available via DTC testing. However, most DTC tests that are currently available do not have the same clinical utility as those offered in the NHS.
Cost of the test Upfront financial investment undertaken by an individual in purchasing the genetic test. The impact of test cost on uptake may be limited to tests not currently available via the NHS, and to individuals who do not meet NHS criteria for test access but perceive the personal utility to be high and have the ability to pay. If the technological costs decrease but access to genetic tests via the NHS remains limited to those who meet eligibility criteria, the risk of information asymmetry and associated anti-selection may increase substantially.
What is the impact of the condition in terms of the length and quality of life of people who develop it?
Penetrance Likelihood that specific forms of a gene or genes (genetic variants) will be expressed in an individual and lead to development of the condition. For a condition to be important for medical underwriting in insurance, it must have high penetrance. Capacity to assess penetrance depends on the type of conditions being tested. For example, for conditions determined by a large number of genes, the likelihood of developing the condition is more challenging to estimate.
Age of onset Age range in which the condition being predicted by the genetic test usually occurs.

The age of onset of a condition may affect anti- selection of insurance. For example, an individual at risk of an early onset condition may purchase insurance earlier than they may have otherwise done or, conversely, an individual at risk of a late onset condition may delay seeking insurance.

Also, consumers may be able to anti-select if they have reason to believe that they are subject to a late onset condition that has presented no symptoms at the time of purchasing insurance.

Prognosis and morbidity Prognosis is the time from development of the condition to death, while morbidity refers to the consequences for quality of life and/or the health of the individual who develops the condition. Conditions with a high mortality rate (combined with a lack of effective treatment) are important for insurance underwriting, but the time from diagnosis to death and the health state during those years are also important as there may be implications for employment and health and/or social care, which may also have implications for insurance.
Prevalence Proportion of people within a population who develop the condition being tested. Conditions with high prevalence may have a large overall financial impact on insurers. However, conditions with low prevalence may also have an impact if people who are at high genetic risk are disproportionally likely to purchase insurance or make an insurance claim.
What is the potential for reducing the risk of developing the condition and managing its effects if it develops?
Potential for risk reduction and/or treatment

Risk reduction includes interventions delivered before an individual develops symptoms of a condition or when they have developed early symptoms and prevention may still be possible.

Treatment strategies are interventions delivered to people after they have developed a condition, with the aim of reducing its impact on their quality of life and/or life expectancy.

Risk reduction approaches may lead to overdiagnosis and overtreatment, a situation in which an asymptomatic individual is identified as being at high risk of a condition that would not have discernible consequences for them during their lifetime but triggers clinical interventions, which may have an impact on critical illness and medical insurance providers.

Conditions for which treatments are available may have implications for medical insurers, while those for which there is no effective treatment present the greatest risk in terms of life insurance.

Effectiveness and engagement

Risk reduction effectiveness is the capacity of a strategy to reduce an individual's risk of developing a condition.

Treatment effectiveness is the effect on an individual's prognosis and morbidity.

Engagement is the extent to which an individual uses an intervention, which may affect its effectiveness.

The effectiveness of an intervention and the extent to which individuals engage with it are key influences on whether risk reduction or management are feasible for a health condition. Many insurance companies encourage their customers to lead healthy lifestyle and offer financial rewards for doing so (e.g. reduced premiums or discounts on services), but evidence for the impact of risk reduction strategies following genetic tests is mixed and dependent on the condition tested for.
Intervention costs Financial investment required to carry out an intervention. If an individual is identified as being at genetic risk of a condition, the cost of providing them with risk reduction interventions and treatment if the condition develops will have an impact on the risk a genetic test poses to the insurance industry. This risk will be greatest when the cost of treatment is high, particularly in the absence of preventative interventions.


Chara Williams (Designer), Lee Floyd (Developer), and James Gazis (Producer)

Testing the width of the div to see how width it shoudl be.

Thom Shanker, photo courtesy of Chad J. McNeeley

Thom Shanker

Photo courtesy of Chad J. McNeeley