{"id":260591,"date":"2025-07-13T03:38:40","date_gmt":"2025-07-13T03:38:40","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/260591\/"},"modified":"2025-07-13T03:38:40","modified_gmt":"2025-07-13T03:38:40","slug":"the-nhs-plan-to-genetically-test-all-newborns-sounds-smart-until-it-creates-patients-who-arent-sick","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/260591\/","title":{"rendered":"The NHS plan to genetically test all newborns sounds smart \u2013 until it creates patients who aren\u2019t sick"},"content":{"rendered":"

By 2030, every baby born in England could have their entire genome sequenced<\/a> under a new NHS initiative to \u201cpredict and prevent illness\u201d<\/a>. This would dramatically expand the current heel-prick test, which checks for nine rare genetic conditions, into a far more extensive screen of hundreds of potential risks.<\/p>\n

On the surface, the idea sounds like an obvious win for public health: spot problems early, intervene sooner and save lives. But genetic testing on this scale carries real risks, especially if the results are misunderstood or poorly communicated.<\/p>\n

The new plan builds on a recent NHS pilot study<\/a> that sequenced the genomes of 100,000 newborns in England to identify more than 200 genetic conditions. However, these tests don\u2019t provide clear cut answers. They don\u2019t offer a diagnosis or certainty, just an estimate of risk.<\/p>\n

A genetic result might suggest a child has a higher (or lower) probability of developing a certain disease later in life. But risk is not prediction. If parents, or even clinicians, misinterpret that nuance, the consequences could be serious.<\/p>\n

Some families may come to see a child flagged as \u201cat risk\u201d as a patient-in-waiting. In extreme cases, they may treat a probability as a certainty; assuming, for instance, that a child \u201chas the gene\u201d and will inevitably become ill. That assumption could reshape how children are raised, how they\u2019re treated and how they could see themselves.<\/p>\n<\/p>\n

Alarming language<\/p>\n

This isn\u2019t speculation. Research shows<\/a> that while some people understand risk scores accurately, many struggle with statistical information<\/a>. Words like \u201chigh risk\u201d or \u201clikely\u201d are interpreted differently by different people and often more seriously than intended<\/a>. Even trained doctors can overestimate<\/a> what a positive test result means. When it comes to genomics, the line between \u201cyou might get sick\u201d and \u201cyou will get sick\u201d can blur quickly.<\/p>\n

Policymakers haven\u2019t helped this confusion. Government messaging refers to \u201cdiagnosis before symptoms even occur\u201d<\/a> and \u201cleapfrogging disease.\u201d<\/a> But this language overpromises what genomic data can do and downplays its uncertainty.<\/p>\n

When testing is indiscriminate and communication unclear, the fallout can be wide ranging. Children identified as \u201chigh risk\u201d may undergo years of monitoring, unnecessary medical appointments, or even treatment for diseases they never develop. In some cases, this leads to physical harms, from unnecessary medications to procedures with side effects. In others, the damage is psychological: shaping a child\u2019s identity around an anticipated future of illness. These psychological effects can be lasting. Being told you\u2019re likely to develop a condition like dementia may influence how a person plans their life<\/a>, even if that illness never materialises.<\/p>\n<\/p>\n

False positives<\/p>\n

There are also broader issues with applying this kind of screening to everyone. Risk based testing works best when it\u2019s targeted; for example, among those with symptoms or a strong family history. But in the general population, where most people are healthy, false positives<\/a> can far outnumber accurate results. Even well designed tests can produce misleading outcomes when applied at scale.<\/p>\n

This is a well-known statistical effect<\/a>, discussed during the COVID pandemic<\/a>. In populations where a disease is rare, even highly accurate tests produce more false positives than true ones. If DNA screening is rolled out universally, many families will be told their child is at risk when they are not. These false positives can lead to a cascade of further tests, stress and unnecessary clinical interventions; all of which consume time and resources and may cause real harm.<\/p>\n

This issue already affects adult testing. For example, Alzheimer\u2019s tests that measure early changes in the brain work well in memory clinics, where patients already show symptoms. But when these same tests are used on the general population, where most people are healthy, they produce false positives in up to two-thirds of cases<\/a>. If genetic screening in newborns is rolled out in the same way, it could lead to similar problems: mislabelling healthy children as sick, and causing unnecessary worry and follow-up tests.<\/p>\n

So what\u2019s the solution? It\u2019s not to abandon genetic testing altogether \u2013 far from it. When used carefully, genomic data can offer real benefits, particularly for patients with symptoms or in research settings. But if we\u2019re going to roll this out to every newborn, the surrounding infrastructure needs to be robust.<\/p>\n

That includes:<\/p>\n