It’s not been a good week for followers of conventional wisdom. The official lines on face masks and long Covid have (once again) been brought into question and now Government figures have shown (also not for the first time) that hospitalisation numbers are being skewed by the fact that almost a quarter of ‘Covid inpatients’ in England are actually in hospital for a different reason.
Given that “increasing Covid hospital admissions” could trigger the Government’s ‘Plan B’ of mask mandates and vaccine passports (and even perhaps ‘Plan C’ of another lockdown), the implications of this distortion of the truth could be huge. MailOnline has more.
Health service statistics show there were 6,146 NHS beds taken up by people who were Covid positive on September 14th, the latest date data is available for.
But just 4,721 patients (77%) were primarily being treated for the virus, with the remaining 1,425 receiving care for other illnesses or injuries. They could include patients who’ve had a fall or even new mothers who tested positive after giving birth.
In NHS hospitals in the Midlands, around a third of Covid patients were mainly being treated for another reason on September 14th.
Separate NHS figures suggest as many as half of daily hospitalisations only test positive after being admitted for a separate condition.
Hospital numbers have become the key metric for ministers and their scientific advisers, now that vaccines have taken the emphasis away from infection numbers.
Boris Johnson has said lockdown curbs may have to be reintroduced if Covid hospital numbers rise sharply as part of his winter blueprint to tackle the virus, which could see masks and working from home mandated again.
But he did not put a firm figure on the threshold that would trigger the return of restrictions when he announced the contingency plans earlier this week.
The latest figures suggest the standard Covid hospital numbers have become a less reliable way of gauging the outbreak and NHS pressure. [Have they ever been reliable?]
Worth reading in full.
