Mapping the Global Burden of Disease project’s Summary Exposure Values by state for 2019

As part of a current project with Calvin Isch and Richard Brown, I’ve been looking at the Global Burden of Disease project’s Summary Exposure Values (SEVs). The SEV is a really useful measure for us, because we’re interested in the extent to which people’s perceptions of risk are associated with objective measures of exposure to health risks. Luckily, the Global Burden of Disease project have done some incredibly detailed work to try to quantify exposures to certain risks.

The GBD describe the SEV as follows:

“A measure of a population’s exposure to a risk factor that takes into account the extent of exposure by risk level and the severity of that risk’s contribution to disease burden. SEV takes the value zero when no excess risk for a population exists and the value one when the population is at the highest level of risk; we report SEV on a scale from 0% to 100% to emphasize that it is risk-weighted prevalence.”

I would recommend this excellent Lancet paper for more details on the construction of this measure.

Since I’m particularly interested in perceptions about uncontrollable mortality risks (risk exposures which are not impacted by individual behaviour), I’ve been using the SEV values for environmental and occupational risks, which is a combined index of those risks not classified by the GBD project as being related to behaviour (see for more on the data).

This all seemed like an excellent excuse to make a new map, this time using Leaflet, an excellent R package, which allows you to create and customise interactive web maps without knowing any JavaScript. Here’s the resulting map. To see the interactive version, please click on the image link and view the map in RPubs, as WordPress won’t allow me to embed it using an iframe.

A map of the Global Burden of Disease (GBD) Project’s summary exposure values (SEV) by state for the USA in 2019.

Measuring perceived uncontrollable mortality risk: poster, papers & video

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 (2021)

UPDATE! New video

Since I originally posted this, my fantastic PhD student, Richard Brown has created a video, explaining the findings of our paper in the Journal of Public Health. Richard’s creation won him the best talk prize at the Northumbria University Early Career Researchers’ conference, 2021.

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:


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


New book out now!

Applied Evolutionary Anthropology: Darwinian Approaches to Contemporary World Issues

AEA Book Cover

This book has been published as the first of a series, Advances in the Evolutionary Analysis of Human Behaviour, edited by Rebecca Sear. The series is intended “to promote the European Human Behaviour and Evolution Association tenets of rigour, integration and pluralism by producing a series of methodologically rigorous books with a pluralistic theme in the human behavioral sciences.”

This volume was edited by Mhairi Gibson and David Lawson. I am proud to have made my contribution, along with with Daniel Nettle:

Chapter 10Socioeconomic Disparities in Health Behaviour: An Evolutionary Perspective.

Video: Being there (talk at the ESRC Festival)

Being there: a brief visit to a neighbourhood induces the social attitudes of that neighbourhood

Here is a video of one my recent talks as part of the ESRC Festival of Social Science event, “Using social sciences to tackle the toxicity of urban life”. The video just gives footage of me talking, so you will need to download the slides as a PDF here: beingtherepresentationslides.pdf.

The publication associated with this presentation can be found at: