Posted on 04/19/2020 11:08:37 AM PDT by SeekAndFind
UPDATED 17th April 2020
Lay Summary by Mandy Payne, Health Watch
This page is updated daily as new information emerges. It sets out the current Case Fatality Rate (CFR) estimates, the country-specific issues affecting the CFR, and provides a current best estimate of the CFR, and more importantly, the Infection Fatality Rate (IFR).
The IFR estimates the fatality rate in all those with infection: the detected disease (cases) and those with an undetected disease (asymptomatic and not tested group).
The total number of cases and the total number of deaths from COVID-19 outbreak data was drawn down (scraped) from https://www.worldometers.info/coronavirus/.
The proportion of deaths to the total numbers of cases was meta-analysed using the R function metaprop, using fixed-effect inverse-variance weighting. Estimates from the cruise ship ‘Diamond Princess’ as well as countries with fewer than 1000 cases are excluded from the analysis. (updated 9th April). We present country-level case fatality as a percentage along with 95% confidence intervals in a forest plot. Estimates of heterogeneity and a 95% prediction interval are presented, but a pooled overall estimate is suppressed due to heterogeneity. (understanding data in meta-analysis)
*case fatality rate is the number of reported deaths per number of reported cases (Updated 16th April)
Between countries, case Fatality rates vary significantly, and over time, which suggests considerable uncertainty over the exact case fatality rates.
In China, the CFR was higher in the early stages of the outbreak (17% for cases from 1 to 10 January) and reduced to 0.7% for patients with symptom onset after 1 February.
Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. Reported in (China CDC weekly). Update 21st March:
The epidemic curve of onset of symptoms peaked around January 23rd to 26th, then began to decline up to February 11th. Most cases were aged 30 to 79 years of age (87%), 1% aged ≤ 9 years, 1% aged 10 to 19 years, and 3% 80 years or older.
The CFR was 2.3% (1023 deaths/44 672 confirmed cases). Reported CFRs by age were
| Age (deaths/cases) | CFR (95% CI) |
| ≤ 9 years (0/416) | 0% |
| 10 to 19 years (1/549) | 0.18% (0.03 to 1.02%) |
| 20 to 49 years (63/19790) | 0.32% (0.25% to 0.41%) |
| 50 to 59 years (130/10,008) | 1.3% (1.1% to 1.5%) |
| 60 to 69 years (309/8583) | 3.6% (3.2% to 4.0%) |
| 70 to 79 years (312/3918) | 8.0% (7.2% to 8.9%) |
| ≥80 years (208/1408) | 14.8% (13.0% to 16.7%) |
Patients with comorbid conditions had much higher CFR rates. Those with no comorbidites had a CFR of 0.9%. Critical cases had a CFR of 49%, no deaths occurred among those with mild or even severe symptoms.
Limitation: some variables (i.e., comorbid condition and case severity) are not required fields in the Chinese CDC Infectious Disease Information System, some records have missing data.
Most acute viral infections have three short-term effects on the CVD system: the inflammatory response can increase the risk of an acute coronary syndrome; depression of the myocardium can worsen heart failure, and inflammation can unmask heart arrhythmias. Seasonal influenza infections can increase CVD deaths significantly, and community-level rises in Influenza-like illness (ILI) lead to rises in CVD mortality:
Nature Medicine: Estimating the clinical severity of COVID-19 from the transmission dynamics in Wuhan, China~:
*Because Wuhan prioritized the admission of more severe cases, the sCFR will be substantially lower than the HFR. *sCFR (s for symptomatic) defines a case as someone who is infected and shows certain symptoms; HFR (hospitalized) defines a case as someone who is infected and hospitalized.
In Italy, there are several reasons why the CFR is higher. The age structure of the Italian population (2nd oldest in the world); highest rates of antibiotic resistance deaths in Europe (Italy tops the EU for antibiotic-resistance deaths, with nearly 1/3rd of the deaths in the EU). Smoking also seems to be a factor associated with poor survival – in Italy, 24% smoke, 28% men. In the UK, for instance, 15% are current smokers.
Coronavirus: Is Covid-19 the cause of all the fatalities in Italy?
Sarah Newy reports Italy’s death rate might also be higher because of how fatalities are recorded. In Italy, all those who die in hospitals with Coronavirus are included in the death counts.
Recording the numbers of those who die with Coronavirus will inflate the CFR as opposed to those that died from Coronavirus, which will deflate the CFR.
Report from the Italian National Institute of Health: analysed 355 fatalities and found only three patients (0.8%) had no prior medical conditions. See Table 1 in the paper; (99% who died had one pre-existing health condition): 49% had three or more health conditions; 26% had two other ‘pathologies’, and 25% had one.
The most common problems in the 355 who died were: 76% high blood pressure; 36% diabetes, and 33% ischemic heart disease.
The average age of deceased and COVID-19 positive patients was 79.5 years (median 80.5, range 31-103). The median age of those that died was >15 years higher than patients who contracted the infection (median age: died 81 years – infected 63 years).
Data comes from cases diagnosed by regional reference laboratories (N = 73,780). Source
Produced by the Istituto Superiore di Sanità (ISS) the data is collected through a dedicated web platform and includes all the cases of COVID-19 diagnosed by the regional reference laboratories. The data are updated daily by Region although some information may take a few days to come through.
Age effects by age band:*
*The Statistical model used is a grouped-binomial logistic regression with log-link function with main effects for age-band and sex (no two-way interaction terms). Deviance statistic is 30.9 on 6 degrees of freedom.)
| Females | Males | |||
| Age band | CFR (%) | 95% CI | CFR (%) | 95% CI |
| 30-39 | 0.26 | 0.16 to 0.42 | 0.43 | 0.27 to 0.69 |
| 40-49 | 0.55 | 0.43 to 0.70 | 0.91 | 0.72 to 1.16 |
| 50-59 | 1.23 | 1.08 to 1.40 | 2.05 | 1.81 to 2.33 |
| 60-69 | 4.02 | 3.71 to 4.34 | 6.67 | 6.22 to 7.15 |
| 70-79 | 11.86 | 11.26 to 12.50 | 19.71 | 18.98 to 20.47 |
| 80-89 | 17.94 | 17.11 to 18.80 | 29.81 | 28.78 to 30.87 |
| >=90 | 19.41 | 18.05 to 20.88 | 32.26 | 30.01 to 34.68 |
| Marginal estimates of the case-fatality rate by age. | ||||
The risk ratios give the ratio of case-fatality rate in one age-band with the case-fatality rate in the reference age-band (here set to age 60-69).
| Category | Risk ratio | 95% CI |
| Age 30-39 | 0.06 | 0.038 to 0.10 |
| Age 40-49 | 0.14 | 0.11 to 0.17 |
| Age 50-59 | 0.31 | 0.27 to 0.35 |
| Age 60-69 (Reference) | 1.00 | – |
| Age 70-79 | 2.95 | 2.7 to 3.2 |
| Age 80-89 | 4.47 | 4.1 to 4.8 |
| Age 90+ | 4.83 | 4.4 to 5.3 |
| Female | 1.00 | – |
| Male | 1.66 | 1.58 to 1.74 |
| Rate ratio estimates (95% CI) for CFR under independence model | ||
For example, a ratio of 4.47 (aged 80 to 89) means that the case fatality rate is 4.47 times higher than those aged 60 to 69. For people aged 40 to 49 the case fatality rate is (1 – 0.14)*100 = 86% lower than in people aged 60 – 69. Similarly, the case-fatality rate for men was 66% higher than the case-fatality rate in women.
| Age band | Cases (n) | Deaths | CFR (%) |
| 18-29 | 474 | 0 | 0.00 |
| 30-39 | 1068 | 0 | 0.00 |
| 40-49 | 1819 | 0 | 0.00 |
| 50-59 | 2193 | 5 | 0.23 |
| 60-70 | 780 | 6 | 0.77 |
| Total | 6334 | 11 | 0.17 |
| Distribution of cases, deaths and case-fatality in health professionals – data is in the consolidation phase and does not include cases with unknown age | |||
Waiting for the FearBros to debunk this study. It should be entertaining.
My God - TMI!
Iceland has tested a higher proportion of people than any other country (9,768 individuals), equivalent to 26,762 per million inhabitants the highest in the world (as a comparison, South Korea has tested 6,343 individuals).
Screening suggests 0.5% are infected; the correct figure is likely higher due to asymptomatics and many not seeking testing: estimates suggest the real number infected is 1%.
Iceland, currently reports two deaths in 963 patients, CFR. 0.21%. If 1% of the population (364,000) is infected, then the corresponding IFR would be 0.05%. However, they have limited infections in the elderly as their test and quarantine measures have seemingly shielded this group, and the deaths will lag by about two weeks after the infection.
Iceland’s higher rates of testing, the smaller population, and their ability to ascertain all those with Sars-CoV-2 means they can obtain. an accurate estimate of the CFR and the IFR during the pandemic (most countries will only be able to do this after the pandemic). Current data from Iceland suggests their IFR is somewhere between 0.01% and 0.19%.
Imperial College, London, modelled the impact of COVID-19, interventions to reduce the spread and case fatality.
Summary of their assumptions: Impact assessment of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand pdf.
Infection Fatality Rate (IFR) estimates were based on Verity et al. and adjusted for a non-uniform attack rate to give an IFR of 0.9% (95% credible interval 0.4%-1.4%).
Published in MedRxiv (preprint and not been peer-reviewed) Verity obtained age-stratified CFR estimates from China and individual data on 1,334 cases identified outside of mainland China. Verity et al. estimated an overall IFR for China of 0.66% (0.39%,1.33%) that increased with age.
Highest peak rate ratios for admissions are in those years where the confirmed simultaneous circulation of Influenza LIke Illness (ILI) and acute bronchitis occur. Between 1990–91 to 2004–05 respiratory admissions of ≥65 years in England and Wales were analysed. The ILI peaked was highly variable: the earliest during mid-November (week 46, 1993–94) and the latest, late February/early March (week 7, 1997–98).
On the Diamond Princess, initial estimates reported six deaths out of 705 who tested positive: CFR of 0.85%. All six deaths six occurred in patients > 70. No one under 70 died.
Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship. Comparing deaths onboard with expected deaths based on naive CFR estimates using China data estimated a CFR 1.1% (95% CI: 0.3-2.4%); IFR 0.5% (95% CI: 0.2-1.2%).
Nature published an update on what the cruise-ship outbreaks reveal about COVID-19
Japanese officials performed > 3,000 tests on the Diamond Princess. Estimating the infection and CFR for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship using the age structure of the onboard population and modelled that on naive CFR estimates using China data reported:
Our current best assumption, as of the 9th April, is the CFR is 0.72% – the lowest end of the current prediction interval and in line with several other estimates.
Evaluating CFR during a pandemic is, however, a very hazardous exercise, and high-end estimates should be treated with caution as the H1N1 pandemic highlights that original estimates were out by a factor greater than 10.
We now want to draw your attention to the flaws in CFR estimation due to the changing nature of the testing regimes.
Italy: A change in strategy on Feb 25 limited testing to patients who had severe signs and symptoms also resulted in a 19% positive rate (21,157 of 109,170 tested as of Mar 14) and an apparent increase in the death rate—from 3.1% on Feb 24 to 7.2% on Mar 17—patients with milder illness were no longer tested.
In the UK, only patients deemed ill enough to require at least one night in hospital met the criteria for a COVID-19 test. Modes. are also starting to accrue that suggest the number of people infected is much higher than what testing alone identifies, and that the number infected is much higher in denser populations.
CFRs across countries are, therefore, highly variable, depending on who is tested for what reasons. There is no consistency. See CFR figures by countries over time:
The current COVID outbreak seems to be following previous pandemics: initial CFRs start high and trend downwards. For example, In Wuhan, the CFR has gone down from 17% in the initial phase to near 1% in the late stage. It is increasingly clear that current testing strategies are not capturing everybody. In South Korea, considerable numbers who tested positive were also asymptomatics- likely driving the rapid worldwide spread.
CFR rates are subject to selection bias as more severe cases are tested – generally those in the hospital settings or those with more severe symptoms. The number of currently infected asymptomatics is uncertain: estimates put it at least a half are asymptomatic; the proportion not coming forward for testing is also highly doubtful (i.e. you are symptomatic, but you do not present for testing). Therefore we can assume the IFR is significantly lower than the CFR.
Emerging evidence suggests many more people are infected. than tested. In Vo Italy, at the time the first symptomatic case was diagnosed, about 3%, had already been infected – most were completely asymptomatic.
We could make a simple estimation of the IFR as 0.36%, based on halving the lowest boundary of the CFR prediction interval. However, the considerable uncertainty over how many people have the disease, the proportion asymptomatic (and the demographics of those affected) means this IFR is likely an overestimate.
In Swine flu, the IFR ended up as 0.02%, fivefold less than the lowest estimate during the outbreak (the lowest estimate was 0.1% in the 1st ten weeks of the outbreak). In Iceland, where the most testing per capita has occurred, the IFR lies somewhere between 0.01% and 0.19%.
Thanks for posting, this is a keeper.
The county I live in here in central Tx. Has 170 cases. 50 of those have presumably recovered, showing no symptoms or fever for 72 hours.But 16 have died. Or almost 10% Some did have other serious health issues,and were in their 80’s. Still left about half though, or around 5%. That isn’t good. I realize it’s just a small sampling, but it’s where I live,and local news.
Quickgun,
Thanks for the update from Central TX... pretty country, especially around the Hill Country area.
Until testing is widespread, we don’t have any idea how many people caught Coronavirus, but showed no symptoms...
What we don't really need is country vs. country statistics.
Bad day to be a fearbro
In my large metro county of over 1M people there has not been a death since April 1. He numbers of new cases have personally ceased to exist
Additionally in the state of a Florida overall the vaunted modelers have cut the likely number of deaths from 3400 to 1700’. You read the right 1700 deaths in a state of 21M people
That means the rare is 0.008%
Imagine
An economy shut down for 1700 deaths
Oh and by they way. Estimated influenza deaths in Florida? 3000
Thank God it’s short lived...I read it burns itself out in 70 days. I’m not panicked about it. I’m just careful and go about my business pretty much as usual, except for I get to work from home all the time now whereas I could only do it a couple of days a week prior.
So as the actual case data, which may itself be inflated depending how they assign what the primary cause of death is,
its best case as bad as the flu, worst case maybe a couple times worse.
OK
Covid-Data Ping.
XX
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