Measuring perceived uncontrollable mortality risk

I’m beginning to build a bank of resources to support people to use the measures of perceived control over mortality risk that Daniel Nettle and I developed some years ago (Pepper & Nettle, 2014). So far, these measures seem to be a good predictor of health behaviour and, in our data, they outperform the Multidemsional Health Locus of Control (MHLC), which is a commonly used measure examining a similar construct.

To get things started, I’ve created my first ever #BetterPoster (see here for more on the “Better Poster” concept) that gives a brief overview of the theory and evidence regarding the relationship between perceived uncontrollable mortality risk and health behaviour. I’m presenting this poster at EHBEA2021, and Richard Brown will be giving a talk on some of the evidence summarised in panel 4 of the poster below. The 2-minute audio recording that accompanies the poster can be downloaded here.

I’ve also created a 1-page guide to using the measure, which gives the question text and an at-a-glance summary of what the responses represent, with references for further details. You can download the guide here.

My #BetterPoster for EHBEA 2021 – a summary of the evidence so far on perceived uncontrollable mortality risk & health behaviour.

The poster above is embedded as an image, so the links don’t work. Here are the links to the key references:

Nettle (2010),

Pepper & Nettle (2014a),

Pepper & Nettle (2014b),

Brown, Coventry & Pepper (2020 – preprint).

New maps: Cross-country relationships between life expectancy, intertemporal choice and age at first birth

In one of my previous posts, I presented some interactive maps, made using Google Fusion Tables, to support a paper on Cross-country relationships between life expectancy, intertemporal choice and age at first birth, written with my collaborator, Adam Bulley. However, Google have since … Continue reading

Looking to convert various statistics to correlation coefficients? Here’s a script I made earlier

In the process of producing a recent meta-analysis on telomeres as markers of exposure to stress and adversity, we needed to convert various effect types (e.g. standardised betas, F-ratios and T-statistics) to correlation coefficients, ready to be meta-analysed. The script is fairly straightforward, but it took a little while to write the script and format the data entry to work smoothly with the script. So, to save others some time and energy, I’ve made a generic version of the script, along with a template input file–just to make things easy to use. You can download the files here, along with a guide on how to correctly enter your data into the template input file, so that the correlation converter script can read it.

Quick-start guide:
1. Enter your data into this template csv, using the table in this document as a guide.
2. Make sure the template csv is in the same folder as this R script.
3. Run the script in R. If you don’t have R, you can learn more and download it here.
4. Once you’ve run the script, a new file, entitled “MetaAnalyisCommonAssociations.csv” should appear in the same folder. It should contain all the data in your original csv, plus two new columns containing the “CommonEffect” (the correlation coefficient) and the “CommonEffectVariance” (the measure of variance).

What is the script doing? The script contains comments explaining what each piece of code does, but for those who find diagrams and equations easier to follow, this flow chart shows the operations it performs:

EffectConversionFlowChart

Videos: Why should medics care about evolution?

A friend of mine, the wonderful Thomas Carpenter, is currently studying medicine at Edinburgh University. He is part of a group of medical students with an interest in evloution, who are calling themselves the Evolutionary Medics.

The Evolutionary Medics recently ran an event, which was entitled “Bringing Darwin back to Edinburgh: wine, cheese and evolutionary medicine.” The excellent presentations are now available to view on YouTube and I wanted to promote them here:

Dr Sam Brown – Can we make evolution proof drugs?

Prof Gillian Bentley – The Clinical Significance of Evolutionary Medicine