Here, you can read Richard’s interview with me for The Psychologist. We chat about the research I’ve been doing for the past decade or so – research which Richard is now moving forward as part of his PhD.
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 https://ghdx.healthdata.org/ 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.
The RcPsych EPSIG aims to raise awareness of the value of evolutionary theory to psychiatry, as well as encouraging research on the topic. The group has a fascinating mix of members with a range of expertise, bringing together clinical and academic expertise, across disciplines. They hold talks and host symposia, some of which can be found on their YouTube channel. Their newsletters are also available to all, if you want to read more.
People – especially students – get in touch with me on a surprisingly regular basis to ask for the data and shape files I used in my previous post on mapping methods of travel to work in Newcastle upon Tyne.
Unfortunately, since Google discontinued their support for Fusion Tables, people are now unable to download the data and shape files from the maps I created. Never fear! You can find them here.
**This link will take you Dropbox, but you don’t need an account to download the file
Getting other Census and geographies data:
What’s even better than being able to simply download the data from this page? Probably, it’s knowing where to get your own data (especially since the 2011 Census data are about to become outdated). There are couple of really handy websites for accessing Census data and information on UK geographies:
The Nomis website provides a useful portal for accessing census data.
The ONS Open Geography Portal provides a lot of helpful products, including shape files for various geographies.
Hopefully these resources will help you to get your projects done. Happy mapping!
A map from the original project, showing the percentage of residents in each LSOA travelling to work by bicycle (in shades of green), with all data represented in the pop-out bubbles in the interactive version.
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 (Brown & Pepper, 2023).
To get things started, I’ve created my first ever #BetterPoster (see here for more on the “Better Poster” concept), giving a brief overview of the theory and evidence regarding the relationship between perceived uncontrollable mortality risk and health behaviour. I presented this poster at EHBEA2021, and Richard Brown gave 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:
The wonderful 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.
Although I understand that many people are suffering from “Zoom fatigue”, an online conference offers a number of fresh advantages. It becomes easier for people from all over the world to participate without time, cost, or carbon footprint concerns becoming barriers. We can be innovative about our scheduling too. Having some pre-recorded talks and posters available in advance of the conference will mean more time to interact with each other on the day. More interaction can mean more ideas, more fun, and more potential for collaboration. Another advantage of having some pre-recorded talks: you can pause, rewind, and watch again! No more wondering if you’re asking a silly question because you didn’t quite hear something that was said earlier on in the talk. Equally, if the topic of the talk isn’t quite as you expected, you can stop watching without fear of disrupting others in the audience. This year’s meeting will enjoy all these advantages, plus some of the buzz of a live event with some live talks and Q&A sessions.
To really boost the interactivity of the conference, we’ll also be running our first ever Evolutionary Medicine and Public Health Grand Challenges! Conference delegates can sign up to work in virtual teams to address the big questions and challenges facing medicine and public health today, with topics ranging from ageing to tuberculosis. The aim of these events is to encourage new connections and collaborations, and to spark innovation in the EMPH community. Check out the ISEMPH-2021 website for further details: https://isemph.org/Grand-Challenges-2021
Delegates at the Inaugural ISEMPH meeting in 2015, in Tempe, Arizona
A little while ago, I was contacted by someone who was looking for a shape file for the Newcastle upon Tyne area, as they wanted to map some data as part of a research project.
It occurred to me that other people might be looking for such a file since, back when I made my maps of transport usage in Newcastle (see previous blog), I’d had to source an ONS shape file of all the LSOAs in England and then manually edit it down to only those contained within the Newcastle area. Should you wish to avoid doing all that work yourself, here is the file!
It is a KML file showing the Lower Level Super Output Areas (LSOAs) in Newcastle Upon Tyne (2011 boundaries). Click here to download.
Example map, based on the KML file provided above, showing percentage of people walking to work by LSOA.
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 →
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:
As part of our recent study on Cross-country relationships between life expectancy, intertemporal choice and age at first birth, my colleague Adam Bulley and I used data from the International Test of Risk Attitudes (INTRA) survey, by Wang, Rieger & Hens … Continue reading →
Gillian Pepper 