Why are Covid-19 death rates hard to calculate?

IN a press briefing on March 3, 2020, the Director-General of the World Health OrganiSation (WHO), Dr Tedros Adhanom Ghebreyesus, reported a 3.4 per cent mortality rate from Covid-19. But how reliable is this figure? We take a look at what British experts have to say on the matter.
In the media briefing, Dr Ghebreyesus reported a total of 90,893 cases of COVID-19 globally, with 3,110 resulting in death.
Dr Ghebreyesus also made some comparisons with the common flu in his statement. These included the facts that COVID-19 “does not transmit as efficiently” as influenza, there are no vaccines and therapeutics for COVID-19, and containment strategies could work for COVID-19 but would not be possible for the flu.
An additional and important observation that Dr Ghebreyesus made regards the severity of the two illnesses. Unlike the flu, nobody is immune to COVID-19. As a result, more people are prone to infection, and many are susceptible to “severe disease.”

Various experts have weighed in on the challenges of calculating an accurate mortality rate for COVID-19.

But how was this mortality rate calculated? And what are the unique challenges of determining the death rate during an epidemic?
Some of the world’s leading health experts have weighed in. Below, we summarize their opinions.
Why calculating the death rate is so ‘tricky’
“It is surprisingly difficult to calculate the ‘case fatality ratio,’ or death rate, during an epidemic,” says John Edmunds, a professor in the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine in the United Kingdom.
This difficulty is due to the long period between the onset of the illness and the fatality, explains Prof. Edmunds.
For COVID-19, this length of time is 2–3 weeks or more, he says. Therefore, to calculate the case fatality rate, we should use the number of confirmed cases from a few weeks ago, rather than at the present time.
Experts define the case fatality rate as “the ratio of deaths occurring from a particular cause to the total number of cases due to the same cause.”
But, continues Prof. Edmunds, in the case of a “rapidly expanding epidemic,” the number of cases from a few weeks ago will always be much smaller than the current one, so “the true case fatality ratio will be higher.”
On the other hand, another bias evens the scale in the opposite direction.
“We do not report all the cases,” says Prof. Edmunds. “In fact, we only usually report a small proportion of them. If there are many more cases in reality, then the case fatality ratio will be lower.”
In conclusion, estimating the true case fatality ratio is “tricky,” says the researcher.
Why 3.4 per cent is likely an overestimate
Dr Toni Ho, a consultant in infectious diseases at the Medical Research Council (MRC)–University of Glasgow Centre for Virus Research, UK, echoes similar sentiments.
She goes on to suggest that the figure of 3.4 per cent is likely an exaggeration, mainly due to the challenges of calculating mortality rates outlined above.
“The quoted mortality rate of 3.4 per cent is taken from confirmed deaths over total reported cases. This is likely an overestimate, as a number of countries, such as the United States (112 confirmed, 10 deaths) and Iran (2,336 cases, 77 deaths), have had limited testing. Hence, few of the mild cases have been picked up, and [the total number of cases] we are observing is the tip of the iceberg.”
In fact, the overestimation could be 10 times higher than the reality, notes Mark Woolhouse, a professor of infectious disease epidemiology at the University of Edinburgh, U.K.
Importance of regionality and other factors
Another factor that confuses the calculations is regionality. “[T]he number of reported cases and deaths is likely to vary depending on the population in question,” says Tom Wingfield, a senior clinical lecturer and honorary consultant physician at the Liverpool School of Tropical Medicine, U.K.
“For example, earlier in the outbreak, reported COVID-19 cases and deaths from Hubei province were predominantly among people admitted to hospital, which may not have captured less severe cases in the community.”
“[T]he evidence suggests that [case fatality ratios] were higher in the earlier stages of the outbreak than in the most recent weeks and higher within than without China.”
Paul Hunter, a professor in medicine at the University of East Anglia (UEA), U.K., also offers his opinion, saying, “We […] don’t know whether the Chinese experience will apply elsewhere — in the U.K., we hopefully won’t have such an intense outbreak in a small area.”
Additional factors that could influence the case fatality ratio include “how cases and deaths are classified,” says Wingfield.
He gives the example of the “spike in cases in China when the case definition was broadened to include those diagnosed clinically rather than confirmed through testing.”
Furthermore, the researcher continues, the case fatality ratio “may change over time during the course of the outbreak.”
“The factors contributing to this may include: mutations in the virus […]; host-related factors, such as immune response of different subpopulations infected; and epidemiological factors, such as levels of exposure and repeated exposure.”
Finally, the actual number of deaths may be underreported, says Wingfield. – Medical News Today

Coronavirus: Scientists use genetic code to track UK spread

SCIENTISTS are analysing the unique genetic code of individual samples from infected patients to track how the coronavirus is spreading across the UK.
Each sample of its genetic material, RNA, reveals another step in the chain of infections – who infected whom.
University of Liverpool scientists can also identify other viruses and bacteria in patients’ throat swabs.
And this may help explain why some patients with no known underlying health conditions become seriously ill.
“The aim is to find out who is getting sick, what kind of illness they have and why – is it the virus that is causing it, is their immune system over-responding or do they have some kind of super-infection?” chief investigator Prof Calum Semple told BBC News.
A similar approach was used to track the Ebola outbreak in West Africa in 2015.
Prof Tom Solomon, director of the Institute of Infection and Global Health, at the University of Liverpool, told BBC News: “We have some patients where we have no idea how they were infected – but by looking at their genetic material and comparing it with others, we can fill the missing link, like a detective story, and that may help control the outbreak in the long term.”
Coordinated at the University of Liverpool, the Clinical Characterisation Protocol involves scientists at several other universities, including Edinburgh, Oxford, Bristol and Glasgow.
Early findings show they can sequence the RNA of the coronavirus and the rest of the respiratory microbiome in eight hours.
Team leader Prof Julian Hiscox, who has been studying another coronavirus, Middle-East respiratory syndrome (Mers), for the past two years, with scientists in Saudi Arabia, told BBC News: “We found with severe Mers cases, people tend to die because they have other co-infections as well, such as klebsiella, which can cause pneumonia.
“We can now identify all the underlying microbiome, the viruses and bacteria in the patient samples, so we can feedback to clinicians that they may have a bacterial infection which could be treated with antibiotics.”
Prof Hiscox’s team are using MinION, a hand-held sequencer developed by Oxford Nanopore Technologies. -BBC
Mers, which emerged in 2012 , has caused more than 800 deaths.
It has a mortality rate of one in three, far higher than Covid-19, but is far less contagious.

Denied a transplant because her bladder makes alcohol

THE patient, a 61-year-old woman, insisted she had not been drinking, even though multiple tests found alcohol in her urine.
The stakes were high. She had come to the University of Pittsburgh Medical Center (UPMC), United States, seeking a liver transplant.
Another hospital’s transplant programme had rejected her because of the positive urine tests, and now, UPMC’s transplant team was also refusing to add her to their waiting list.
Instead, they referred her for treatment for alcohol addiction.
Soon after that, in early 2019, Dr Andrea DiMartini, a psychiatrist with the transplant centre, came with one of her students to the office of Dr Kenichi Tamama, a clinical pathologist and medical director of the UPMC Clinical Toxicology Laboratory.
They said the woman denied drinking and did not seem impaired. Could he take another look at the lab results?
He says what he saw led him to identify a new medical condition that he and his team want to call “auto-brewery syndrome” or “bladder fermentation syndrome”.
Essentially, the woman’s body was making alcohol by itself.
The findings, Dr Tamama said, have implications for programmes that test urine for alcohol, including addiction treatment and transplant programmes.
A description of the case was published on Feb 24 (2020) in the Annals of Internal Medicine.
Dr Tamama noticed that the woman’s blood tests were negative for alcohol.
By itself, this was not proof she hadn’t been drinking. As the body metabolises alcohol, it clears it from the blood faster than from urine.
Then he looked at the results of tests for two by-products or metabolites of alcohol: ethyl glucoronide and ethyl sulphate.
If she had been drinking, they should have been in her urine. They weren’t.
He also noticed that the patient had yeast in her urine. And she had uncontrolled diabetes, a sign that sugar levels in her urine would be high.
“I just tried to put these pieces of the puzzle together, ” he said.
Dr Tamama had heard of rare cases of “gut fermentation syndrome”, in which alcohol is produced in the gastrointestinal tract.
In those cases, patients were intoxicated and their blood had alcohol in it.
There had also been reports of doctors who had fermented urine in a lab.
Dr Tamama and his team wondered if something similar could be happening in this patient’s bladder. But how could they prove it?
He wanted to make sure that the patient was treated fairly. He worried that she might have been “falsely blamed as an alcohol abuser. If that’s the case, it is extremely unfair for that patient”.
They got a fresh urine sample from the patient and had it rushed on ice to the lab. It arrived smelling of alcohol.
Then Dr Tamama let it sit in the lab for 24 hours at various temperatures. At 37°C, the alcohol level increased by a “massive” 18 times, he said.
That was proof that yeast in the woman’s urine was converting sugar to alcohol.
He added that the “alcohol smell is intensified – like wine”.
The researchers were “stunned”.
The yeast was Candida glabrata, which is commonly found in humans. It is closely related to brewer’s yeast, Saccharomyces cerevisiae, the study said.
“The experience we describe here of two liver transplant teams at different institutions demonstrates how easy it is to overlook signals that urinary auto-brewery syndrome may be present, ” the study said.
The team called for standardised guidelines for alcohol abstinence monitoring.
Dr Tamama said he expects that auto-brewery syndrome will be rare.
The case report said the patient was reconsidered for a transplant. “This is a game-changer for the patient, ” Dr Tamama said.
A UPMC spokeswoman said she did not have permission to discuss what has happened to the patient since then. – Stacey Burling/The Philadelphia Inquirer/Tribune News Service

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