Evidence map›Paper›PMID 36237999›Full record

ArticleRand health quarterly2022

Assessing the Impact of Developments in Genetic Testing on Insurers' Risk Exposure.

Daniela Rodriguez-Rincon, Sarah Parkinson, Lucy Hocking, Hamish Evans, Emma Hudson, Katherine I Morley

Open access · greenAbstract read
In one paragraph

Article in Rand health quarterly, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Daniela Rodriguez-Rincon
Sarah Parkinson
Lucy Hocking
Hamish Evans
Emma Hudson
Katherine I Morley

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predictive genetic testing provides individuals with information about their future risk of developing health conditions. Theoretically, predictive genetic tests could have positive or negative impacts on the insurance industry. If genetic test results stimulate actions to reduce health risks, they may reduce costs to insurers. If disclosed to insurers, such information may allow them to better understand individual- and population-level risks and make insurance more affordable. However, if individuals who know they are at high genetic risk of becoming ill or dying are more likely to apply for insurance than those not at high risk, this may lead to an unanticipated increase in claims. It may be exacerbated if people at low genetic risk are less likely to apply for insurance compared to the general population. If this happened on a large scale it could make the insurance market unsustainable. Determining whether a genetic test could affect the insurance industry is complex and needs to be evaluated on a per-test basis. The Cambridge Centre for Health Services Research, a collaboration between RAND Europe and the University of Cambridge, developed a framework for evaluating the potential impacts on the UK insurance industry arising from predictive genetic tests. It considers the characteristics of genetic tests and behavioural aspects that influence their uptake. It is intended to provide a transparent approach for evaluating whether a specific condition for which a test is available could impact the insurance industry, currently or in the future, and understanding the key factors that influence this.

Indexed as

GeneticsHealth InsuranceHealth ScreeningScience, Technology, and Innovation PolicyUnited Kingdom

Identifiers

PMID36237999
PMCPMC9519094
OpenAlexW4210660955

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.