If you’re vaxxed, you’re more likely to be killed by lightning than die of COVID: study




Those odds can be gauged from a study by researchers at the National Institutes of Health, published by the Centers for Disease Control and Prevention. They tracked more than 1 million vaccinated adults in America over most of last year, including the period when the Delta variant was surging, and classified victims of COVID according to risk factors such as being over 65, being immunosuppressed or suffering from diabetes or chronic diseases of the heart, kidney, lungs, liver or brain.

The researchers report that none of the healthy people under 65 had a severe case of COVID that required treatment in an intensive-care unit.

 Not a single one of these nearly 700,000 people died, and the risk was minuscule for most older people, too. Among vaccinated people over 65 without an underlying medical condition, only one person died.

In all, there were 36 deaths, mostly among a small minority of older people with a multitude of comorbidities: the 3% of the sample that had at least four risk factors.

Author(s): John Tierney

Publication Date: 8 Feb 2022

Publication Site: NY Post

Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK

Link: https://www.thelancet.com/journals/landia/article/PIIS2213-8587(18)30288-2/fulltext



3 632 674 people were included in the full study population; the following results are from the analysis of never-smokers, which comprised 1 969 648 people and 188 057 deaths. BMI had a J-shaped association with overall mortality; the estimated hazard ratio per 5 kg/m2 increase in BMI was 0·81 (95% CI 0·80–0·82) below 25 kg/m2 and 1·21 (1·20–1·22) above this point. BMI was associated with all cause of death categories except for transport-related accidents, but the shape of the association varied. Most causes, including cancer, cardiovascular diseases, and respiratory diseases, had a J-shaped association with BMI, with lowest risk occurring in the range 21–25 kg/m2. For mental and behavioural, neurological, and accidental (non-transport-related) causes, BMI was inversely associated with mortality up to 24–27 kg/m2, with little association at higher BMIs; for deaths from self-harm or interpersonal violence, an inverse linear association was observed. Associations between BMI and mortality were stronger at younger ages than at older ages, and the BMI associated with lowest mortality risk was higher in older individuals than in younger individuals. Compared with individuals of healthy weight (BMI 18·5–24·9 kg/m2), life expectancy from age 40 years was 4·2 years shorter in obese (BMI ≥30·0 kg/m2) men and 3·5 years shorter in obese women, and 4·3 years shorter in underweight (BMI <18·5 kg/m2) men and 4·5 years shorter in underweight women. When smokers were included in analyses, results for most causes of death were broadly similar, although marginally stronger associations were seen among people with lower BMI, suggesting slight residual confounding by smoking.


Krishnan Bhaskaran, PhD
Prof Isabel dos-Santos-Silva, PhD
Prof David A Leon, PhD
Ian J Douglas, PhD
Prof Liam Smeeth, PhD

Publication Date: 1 December 2018

Publication Site: The Lancet

Number Needed to Treat (NNT): Just Another Magic Number?

Link: https://towardsdatascience.com/number-needed-to-treat-nnt-7950d84acc8f


Nomograms are a trending term in evidence-based medicine, and COVID-19 research is no exception. In this context, a nomogram is usually a web-based tool, a graphic interface, or an on-line calculator in which patient data on several variables is entered as input, and a single summary statistic is calculated as output, such as the likelihood of successful response to treatment. Many medical researchers and data scientists have put forward nomograms derived from multivariate clinical progression models, to assist in decisions about COVID-19 triage.

Is this enthusiasm for reducing complex clinical decisions to the use of multivariate calculators a leap forward in personalized medicine, enabled by modern computing? There is a sketchy “black box” side to all this, to say nothing of the risk of incorporating statistical design errors or untenable inferential claims into a nomogram being rolled out for immediate, untested use in the middle of pandemic. So let’s treat the history of the “number needed to treat” as a “teachable moment” in the history of nomograms in medicine. What have we learned so far?

Author(s): Savanna Reid

Publication Date: 26 February 2021

Publication Site: Towards Data Science