Plus Russian vaccine data, NSAIDS, death counts, risk stratification and T cell response.
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- An ounce of prevention is worth a pound of cure. Or, to put it in a contemporary context, billions of dollars spent on pandemic prevention and preparedness are worth trillions of dollars in avoided costs.
So says the Global Preparedness Monitoring Board – an independent monitoring and accountability organisation focused on global health crises and founded in 2017 – which has just released its latest report titled ‘A World in Disorder‘.
Like for a child that hasn’t handed in a single piece of homework all year, this report was never going to deliver an A+. The GPMB estimated that the cost of the COVID-19 pandemic so far – more than US$11 trillion – could have paid for 500 years’ worth of appropriate planning and preparedness.
The report is steeped in disappointment that previous warnings about this lack of preparedness had not been heard or heeded. “The Board concludes that little progress has been made on any of the actions called for in last year’s report and that this lack of leadership is exacerbating the pandemic,” the report’s authors wrote. “Failure to learn the lessons of COVID-19 or to act on them with the necessary resources and commitment will mean that the next pandemic, which is sure to come, will be even more damaging.”
- A group of scientists have raised concerns about possible data duplication in the Russian COVID-19 vaccine study published last week in The Lancet.
An open letter written by Enrico Bucci – a systems biologist and bioethicist at Temple University – and co-signed by nearly 40 scientists from around the world, highlighted data patterns that appear to be repeated across the paper.
For example, a group of nine patients given one of the vaccines all appeared to have identical antibody titres at 21 and 28 days.
Speaking to Chemistry World, Bucci said the values for CD4 T cell measurements in one group of volunteers were almost the same as the CD8 T cell measurements for another group of volunteers. Bucci also said the study was underpowered because it tested six different formulations across six groups of volunteers. He noted that while the Oxford vaccine trial provided over 100 pages of supplementary material to support its Lancet paper, the Russian paper only included 22 pages and there wasn’t enough data to verify the paper’s conclusions.
According to Chemistry World, The Lancet has invited the study’s authors to respond to the open letter and is monitoring the situation.
- Non-steroidal anti-inflammatory use does not appear to be associated with increased COVID-19 mortality, hospitalisation or worse outcomes, according to a population-based study from Denmark.
The study, published in PLOS Medicine, used administrative and health registry information to examine outcomes in 9,236 SARS-CoV-2 PCR-positive individuals, 2.7% of whom had filled a prescription for NSAIDs up to 30 days before testing positive. Those who had an NSAIDs prescription were matched to up to four non-users, based on age, sex, relevant comorbidities, other prescription drug use and phase of the outbreak.
Researchers saw no significant differences between users and non-users in 30-day mortality or risk of hospitalisation, intensive care, mechanical ventilation or renal replacement therapy. However there was a significant 55% higher risk of hospitalisation among female users of NSAIDs compared to non-users, but this was not seen among male users.
- How accurately are deaths from COVID-19 being counted? Not very, say the authors of an article in the Annals of Internal Medicine, which explores the challenges associated with certifying deaths from a disease that may have long-lasting health repercussions.
Even counting so-called direct deaths from COVID-19 has not been as straightforward as it might seem: the authors pointed out that the standard case definition for COVID-19 didn’t get finalised in the United States until 5 April, more than four months after the first case was recorded.
The US Centers for Disease Control tried to reduce the underestimation by counting all deaths from pneumonia, flu-like illness and COVID-19, then subtracting from that figure the expected number of deaths from seasonal influenza and pneumonia.
However the authors argue that the concept of a death from COVID-19 needs to be considered in the context of this being a disaster setting, where indirect deaths – for example, due to psychological distress as well as the physiological sequelae of infection – may still be occurring months or even years after the initial event.
“The CDC recommends applying the ‘but for’ principle when ascertaining disaster-related deaths: ‘But for the [pandemic], would the person have died when he/she did?’,” they wrote, describing this as a “simple and feasible intervention” that could significantly improve the reporting of indirect deaths.
- A risk-stratification score based on factors including age, sex, comorbidities, peripheral oxygen saturation and C-reactive protein levels can help predict mortality risk in – and guide management of – patients admitted to hospital with COVID-19.
A paper published in the BMJ reported the outcomes of a prospective observational cohort study, which used data from 35,463 adult patients admitted to hospital with COVID-19 in the UK to develop a set of potential predictor variables for mortality.
The median age of the cohort was 73 years, and overall mortality rate was just over 32%. Of 41 potential variables, the research team identified eight that were the strongest predictors of mortality risk: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, Glasgow coma scale, urea level, and C reactive protein. After selecting cut-off scores for these, they developed the 4C Mortality Score and tested it in a validation cohort of 22,361 patients.
The predicted mortality in this cohort exactly matched the observed mortality – around 30%. When stratified into four levels of risk, the mortality rate for low, intermediate, high and very high risk were 1.2%, 9.9%. 31.4% and 61.5%. The score correctly classified individuals as low-risk more than 99% of the time, and correctly ruled people out of being low risk more than 98% of the time.
“Model performance compared well against other generated models, with minimal loss in discrimination despite its pragmatic nature,” the authors wrote.
- People who recover from COVID-19 show significant amounts of immune T cells targeted at the SARS-CoV-2 virus, and those with more severe illness show greater and more diverse numbers of these memory T cells, a study has found.
Writing in Nature Immunology, researchers present the results of a study analysing the immunological signatures in blood samples from 42 COVID-19 patients after recovery – 28 with mild disease and 14 with severe disease – and 16 unexposed donors.
While the role that T cells play in the disease response and longer term immunity is still to be explored, T cells are known to play a key role in killing cells infected with other viruses such as measles, influenza and hepatitis C.
The study found strong T cell responses to a range of SARS-CoV-2 proteins, including – but not limited to – the spike protein, in all the COVID-19-infected patients. However those with severe disease had stronger and broader responses than those with mild disease.
The authors speculated that the fact the more severely-affected patients showed greater T cell responses could be the result of them having higher viral loads during infection, perhaps because their early T cell response was lower and so they didn’t control the virus as well as those who may have had a stronger early T cell response.
“Alternatively, it is possible that the T cell response was itself harmful and contributes to disease severity,” they wrote.
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