Arguments about causal inference in ‘modern epidemiology’ revolve around the ways in which causes can and should be defined. The potential outcomes approach, a formalized kind of counterfactual reasoning, often aided by directed acyclic graphs (DAGs), can be seen as too rigid and too far removed from many of the complex ‘dirty’ problems of social epidemiology, such as social inequalities and racism. If a potential ‘cause’ cannot be manipulated is it sensible to disregard it, relegating it to the ‘not suitable for epidemiology’ category? The use of properly constructed DAGs may aid causal thinking and help plan relevant analyses – Neil Pearce and Debbie Lawlor provide a simple, but excellent discussion of the use of DAGs in their essay review of Causal Inference in Statistics: A Primer by Judea Pearl and colleagues. However, increasingly, DAGs and analyses are constructed by computer programs, such as DAGitty, now available as an R package ‘dagitty’. Useful as such programmes are, the temptation to use evaluations of DAG-dataset inconsistency to generate purely data-driven, post-hoc modifications to DAGs, raises concern about overfitting and biased inference. Continue reading “Causality in Epidemiology – Themed issue”→
Our study, published recently in the IJE, looks at the relationship between experience of violence, in the form of physical assault in the previous 12 months, and premature mortality in a sample of working-age Russian men living in Izhevsk in the Southern Urals.
In contrast, we found that population-based research on the physical health effects of exposure to violence was limited, and we decided to focus on possible associations between assault and mortality in our study.
Metabolic phenotyping, nowadays most often termed metabolomics, is becoming increasingly applied in epidemiology. Recent technological developments, driven by mass spectrometry and nuclear magnetic resonance spectroscopy, have recently resulted in increasing numbers of quantitative molecular applications at an epidemiological scale. The results suggest that these kinds of new technologies are inevitably becoming common in research projects aiming for molecular understanding of metabolic health and diseases. It is also evident from the epidemiological applications that absolute quantification of identified molecular entities is the very key for biomedical applications, not to mention potential clinical translation of metabolomics methodologies and findings. Continue reading “Metabolic Phenotyping in Epidemiology”→
When Michael Bloomberg was elected Mayor of New York City (NYC), he set forth an ambitious agenda to efficiently sync municipal agencies. Improving New Yorkers’ health was part of his motivation. For example, expanding parks and bike lanes would not only improve people’s quality of life and expand transportation options, their presence would also encourage healthy behaviours. So, why not create cross-agency agendas that allow parks with bike lanes to be created on city streets? Innovative thinkers were hired and were given an unusual amount of political capital and logistical support to implement their plans.
World AIDS Day is held on 1 December every year and provides an opportunity for people across the globe to show solidarity with the millions of people living with HIV. This year’s campaign theme is ‘HIV Stigma: Not Retro, Just Wrong’ #HIVNotRetro and you can find out more about how to get involved at https://www.worldaidsday.org/.
Rheumatic Heart Disease (RHD) is caused by a bacterial (streptococcal) throat infection acquired in childhood. Although this type of infection is common and widespread, a small proportion of children so affected go on to develop an inflammatory condition that leads to scarring and narrowing of the heart valves and, in time, heart failure. Early on in the course of the disease the joints may be affected – hence the term “rheumatic”.
Still an important disease At one time Rheumatic Heart Disease was common throughout the UK, Europe and the US; it was the most important cause of heart disease among young adults in Victorian Britain and probably caused the death of Mozart. Although rare now in most developed countries, it remains an important public health problem in many low and middle income countries. The disease is widespread in the Middle East and Asia, and the the poor indigenous populations of some wealthy countries, for example among Australian Aboriginees and New Zealand Maoris. It is particularly prevalent in sub-Saharan Africa, where it is one of the commonest causes of heart disease, typically affecting children or young adults. There it carries a grim prognosis because of the lack of specialised treatment. Continue reading “Smoke exposure in early life and Rheumatic Heart Disease”→
The IJE conference took place in Bristol on 7 October 2016, a one-day, one-off event.
Rodolfo Saracci, as ever bow-tied and in good spirits, did the honours throughout the day. It was under his IEA presidency that Shah Ebrahim and George Davey Smith were hired as IJE editors, and Rodolfo praised their editorial work by likening it to conducting research (“exciting, adventurous, challenging”), and acknowledging that brave decisions have exposed them to the future judgement of historians.
Today marks the end of an era. The International Journal of Epidemiology used to be a typical hotchpotch of isolated papers on worthy subjects. Occasionally, some were interesting, or related to your field. Under Shah Ebrahim and George Davey Smith it became like nothing else: an epidemiology journal you’d happily subscribe to with your own money, and read in the bath. Continue reading “You should totally watch this entire day of the IJE conference”→
Metabolic phenotyping, nowadays most often termed metabolomics, is becoming increasingly applied in molecular epidemiology. Recent technological developments resulting in increased numbers of quantitative molecular applications of metabolomics triggered the idea for a themed issue of the IJE on Metabolic Phenotyping in Epidemiology edited by George Davey Smith and myself. Continue reading “Metabolic Phenotyping in Epidemiology”→