📢 New paper! We examine intersectional inequalities in neighbourhood air pollution concentration by area deprivation, ethnicity, education, rurality and age. We find evidence of patterns of inequality which depart from an additive framework link.springer.com/article/10.1...
work with me and @drdaveobrien.bsky.social as part of @creativepec.bsky.social! we're recruiting a postdoc to work on the arts, culture and heritage sectors using quantitative methods. please share, please feel free to email me directly with any Qs! jobsite.sheffield.ac.uk/job/Sheffiel...
Want to strengthen your methods training this summer?
Applications are now open for 50+ courses.
• Small classes, personalised instruction
• Speaker Series & social and networking events
• Transferable ECTS credits
Apply now: bit.ly/4qzg7Vr
📢 New publication – and a great way to end the year
In this study, we use MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) to study who is most at risk of violence victimisation in England and Wales. Paper: doi.org/10.1177/1748... 1/n
Come and learn multilevel modelling with me this summer! Or take one of the other excellent courses running as part of Essex Summer School
New paper! doi.org/10.1136/jech...
Using MAIHDA / NHS NDRS cancer data
Among colorectal cancer diagnoses, young, black patients living in deprived areas are more likely to be diagnosed late
17%point gap in late diagnosis rate between the most and least advantaged intersectional strata
Pleased to see this out in print - detailing MAIHDA's desirable statistical properties.
"MAIHDA is especially valuable when inequalities are subtle or data for marginalised intersections are sparse - conditions common in practice"
journals.sagepub.com/doi/10.1177/...
@clarerevans.bsky.social
Want to know how to use MAIHDA to examine heterogeniety in policy outcomes? Here's a nice worked-through example!
How does intersectional identity impact preference for in-person vs online GP appoinments?
Has been great working with @healthfoundation.bsky.social colleagues on this :)
Now officially out with nice formatting and all 🥳 "Thinking clearly about age, period, and cohort effects" -- a gentle introduction to the age-period-cohort problem and how to "solve" it through various types of assumptions.
journals.sagepub.com/doi/10.1177/...
A promotional graphic for the Research Methods Rendezvous, which takes place online on 10 September and 29 October 2025.
NCRM has opened applications to participate in the Research Methods Rendezvous!
This free event will explore the process of turning early-stage ideas into #research projects.
#RMR2025 takes place online on 10 September and 29 October 2025.
Apply: www.ncrm.ac.uk/training/RMR...
Thanks to everybody who chimed in!
I arrived at the conclusion that (1) there's a lot of interesting stuff about interactions and (2) the figure I was looking for does not exist.
So, I made it myself! Here's a simple illustration of how to control for confounding in interactions:>
Our research on the effects that PIP changes could have on local economies, written by authors @nataliecbennett.bsky.social @profbambra.bsky.social & @lukemunford.bsky.social, was featured on the @itvpeston.bsky.social programme last night - you can watch the episode on catch up
Another q for the stats people!
People worry about collinearity (cf blog post below).
Consider a scenario in which the collinear predictors are just controls to account for confounding.
Including both of them doesn't impair the precision with which the effect of interest is estimated, does it?
Multilevel Models: Practical Applications
Curious about multilevel modelling but not sure where to start?
Learn to recognise, build & interpret multilevel models using MLwiN or R, through real-world examples & hands-on practice.
Find out more: bit.ly/3Yj5I4f
#ESS2025
Still time to apply for this RA post- developing and applying the intersectional MAIHDA approach with @clarerevans.bsky.social and others. If you're a quantitative social scientist with an interest in quantitative methods and methods development, please apply! DMs open www.jobs.ac.uk/job/DMR190/r...
Text reads: "An introduction to multilevel modelling for intersectionality research: the MAIHDA Approach. 19th June 2025. Instructors: Prof George Leckie and Dr Andrew Bell. There are logos for Centre for multilevel modelling, NCRM, University of Sheffield, and UKRI
Come join George and I in Bristol, to learn about intersectional MAIHDA! bristol.ac.uk/cmm/software... @ncrm.ac.uk
It’s my first project too! In fact, it’s also my first in epidemiology since I come from philosophy. :) I find stratified graphs based on social categories (like Hernández-Yumar, 2018) really interesting.
A little story about this article & how teachers make a difference:
In 2013, I was a student in a social epidemiology class taught by the esteemed David Williams. He observed, with some frustration while we looked at a series of graphs, that we often lump people into the “Hispanic” category — 1/
George Leckie and I have created a new short MAIHDA tutorial, with videos and practical exercises:
www.ncrm.ac.uk/resources/on...
It's based on our tutorial paper in SSM Pop Health, but shorter and more interactive.
Hope it's helpful! @ncrm.ac.uk
Starting out using #intersectional MAIHDA analyses, so need to dig into this great tutorial paper by @clarerevans.bsky.social , Leckie, Subramanian, @andrewjdbell.bsky.social Juan Merlo 👏
www.sciencedirect.com/science/arti...
New paper published in Social Science and Medicine 'An analysis of intersectional disparities in alcohol consumption in the US'. Led by @sophiebright.bsky.social, this study identifies several understudied groups who may have higher alcohol consumption than traditional methods would suggest.
Abstract Psychological researchers are interested in how things change over time and routinely make claims about, for example, age effects (e.g., personality changes with age) or cohort effects (e.g., differences in intelligence between cohorts). The age-period-cohort identification problem means that these claims are not possible based on the data alone: Any possible temporal pattern can be explained by an infinite number of combinations of age, period, and cohort effects. This concern holds regardless of the study design—it also applies to longitudinal designs covering multiple cohorts—and regardless of the number of observations available—it also applies if we observe the whole population. Researchers rely on statistical models that impose assumptions to pick one specific combination of effects. But these assumptions are often opaque and researchers may be unaware of them, resulting in a lack of scrutiny. Here,...
New preprint! osf.io/preprints/ps...
The age-period-cohort problem is something that many researchers are vaguely aware of. There have been very cool advances in how to reason about it which don't seem to be well-known in psych. So, I've written a primer!
Rereading @andrewjdbell.bsky.social's 2020 paper on APC analysis & this is such a great example of how plots imply certain interpretations of the data.
Same underlying data but depending on how you connect the lines, the implied age effect looks completely different.