The value of direct-to-consumer tests

 Direct-to-Consumer Tests on the Market Today. Identifying Valuable Tests from Those with Limited Utility

For health care professionals, the analytical validity of DTC tests is a primary concern. Analytical validity of DTC genetic testing can be defined by analytical sensitivity and specificity whereby analytical sensitivity is defined as how often a test is positive when the genetic variant of interest is present in the tested sample, and the analytical specificity is defined as how often a test result is negative when the tested sample does not contain the genetic variant of interest.18 A recent study by Tandy-Connor and colleagues19 “indicated that 40% of variants in a variety of genes reported in DTC raw data were false positives” when compared with clinical confirmatory testing. This study highlights the need to scrutinize the analytical validity of DTC genetic testing and consider confirmatory testing in a clinical diagnostic genetics laboratory. 

Per the American Society of Human Genetics, “companies offering DTC genetic testing should disclose the sensitivity, specificity and predictive value of the test, and the populations for the information is known, in a readily understandable and accessible fashion.”

Unfortunately, nobody cares about it, and the regulator is still on vacation.

World Press Photo BCN

Human genome 20 years later

Complicated legacies: The human genome at 20

On genome and precision medicine:

Debates about precision medicine (PM), which uses genetic information to target interventions, commonly focus on whether we can “afford” PM (17), but focusing only on affordability, not also value, risks rejecting technologies that might make health care more efficient. Affordability is a question of whether we can pay for an intervention given its impact on budgets, whereas value can be measured by the health outcomes achieved per dollar spent for an intervention. Ideally, a PM intervention both saves money and improves outcomes; however, most health care interventions produce better outcomes at higher cost, and PM is no exception. By better distinguishing affordability and value, and by considering how we can address both, we can further the agenda of achieving affordable and valuable PM.

The literature has generally not shown that PM is unaffordable or of low value; however, it has also not shown that PM is a panacea for reducing health care expenditures or always results in high-value care (17). Understanding PM affordability and value requires evidence on total costs and outcomes as well as potential cost offsets, but these data are difficult to capture because costs often occur up front while beneficial outcomes accrue over time (18). Also, PM could result in substantial downstream implications because of follow-up interventions, not only for patients but also for family members who may have inherited the same genetic condition. Emerging PM tests could be used for screening large populations and could include genome sequencing of all newborns, liquid biopsy testing to screen for cancers in routine primary care visits, and predictive testing for Alzheimer's disease in adults. These interventions may provide large benefits, but they are likely to require large up-front expenditures.

 

 

Cost-effectiveness of genome sequencing

Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature

It is quite difficult to talk about value in genetic tests without any reference to analytical validity, clinical validity and clinical utility. Once these three issues are appropriately solved, then we need to assess costs. Cost effectivenes makes sense once this three steps are covered successfully.
An analysis of cost-effectiveness of whole genome/exome sequencing it sounds too generic if there is no reference to specific baseline that allows to estimate incremental cost-effectiveness ratios.
That's the reason why a recent article trying to review existing studies fails to achieve any conclusion.

The current health economic evidence base to support the more widespread use of WES and WGS in clinical practice is very limited. Studies that carefully evaluate the costs,
effectiveness, and cost-effectiveness of these tests are urgently needed to support their translation into clinical practice.

 Let's start focusing on the assessment of three key perspectives before entering into a black hole.

Integrating genome and epigenome studies

The Key Role of Epigenetics in Human Disease Prevention and Mitigation

I've said it many times: beware of snake-oil sellers. Nowadays you may find it everywhere, specially on internet. You may get a genetic test for a disease that creates a false illusion of safety, or another that provides an unnecessary and avoidable concern. Only evidence based prescribed tests can be considered appropriate.
Therefore, if you want to confirm that genome is not enough, you have to check the review at NEJM on epigenetics. At the end of the article you'll find the explanation on why we do need integrated genome and epigenome association studies. You'll understand that cancer is fundamentally an epigenetic disease.
The current knowledge is changing quickly some conventional truths and "known unknowns" that we've had for years. This is good news for citizens, and bad news for snake-oil sellers if detected. Governments should help citizens on this screening effort, and protect citizens from fake medical information.

Challenges in Cost-Effectiveness Analysis of genomic tests

From: Cost-effectiveness analyses of genetic and genomic diagnostic tests (Must read)

Type of challenge	Example of challenge	Description of challenge
Methodological	Selecting the appropriate evaluative framework	Is the standard extra-welfarist view and use of CEA appropriate, or should the distinct theoretical approach reflecting the welfarist view and use of CBA be adopted to allow consequences other than health gain, such as the value of diagnostic information from the genomic-targeted diagnostic test, to be valued?
	Relevant study perspective	Is the standard recommendation to focus on the use of health-care services appropriate when the genomic-targeted diagnostic test may provide information that affects the use of other services, such as education or employment?
	Relevant time horizon	Is a lifetime sufficient when the impact of a genomic-targeted diagnostic test may extend to infinite time horizons that are not limited by the lifespan of one individual?
	Defining the relevant study population	Is the standard definition of a patient (the person receiving the technology) appropriate when there could be spillover effects to family members (currently alive or to be born) as a result of information from a genomic-targeted diagnostic test?
	Valuing consequences	Is identifying and measuring the impact on health status alone sufficient to capture the (good and bad) consequences of a genomic-targeted diagnostic test?
Technical	Variation in the individual characteristics of the relevant study population	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily capture the impact of individual patient variation within a population with different genotypes and phenotypes
	Number of diagnostic and, if appropriate, subsequent treatment pathways	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily account for multiple comparators often needed when evaluating a new genomic-targeted diagnostic test
	Capturing impact of reduced time to diagnosis	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot account for the impact of reduced time to achieve a diagnosis, which is often a proposed benefit of a genomic-targeted diagnostic test
	Capturing impact of capacity constraints	Decision analytic model-based CEA currently assumes limitless capacity within health-care systems, which is often not a reasonable assumption when introducing a genomic-targeted diagnostic test to populations for whom a diagnosis was not previously available
Practical	Availability of data	There is often a lack of data available to populate decision analytic model-based CEA
	National tariff of test cost	No national tariff for genomic-targeted tests exist
Organizational	Complex health-care systems	Decision analytic model-based CEA assumes that money saved and benefits accrued are transferable, but this is often challenging in complex health-care systems that comprise an overarching funding mechanism (public, private, insurance), a service and staffing model for providing care for different sectors (community, general practice, hospital, specialist) and a means of allocating funding to these different sectors
	Generalizability of results	Decision analytic model-based CEA is relevant only to the defined decision problem, and decision-makers who want to use the results must decide whether the focus of the analysis is relevant to their own jurisdiction
	Expensive nature of health technology assessment	Decision analytic model-based CEA conducted within national health technology assessment processes requires considerable funding and expertise that are not available to all, which may contribute to the inequity in access to new genomic-targeted diagnostic tests across the world

1. CBA, cost-benefits analysis; CEA, cost-effectiveness analysis.