Tracing a science story
The Herald has a headline Two or more children? You’re at risk of heart disease. The story does have a link, but it’s to the Daily Mail, which (unsurprisingly) has no further information about sources.
Searching for key words (“china heart disease risk number of children“) leads to a story at Science Daily. It also doesn’t link or specify enough information to find the research. However, it does indicate that the origin is some sort of commentary from someone at the European Society of Cardiology, involved with their guidelines on “Management of CVD During Pregnancy.”
Searching for ‘“management of CVD During Pregnancy” esc‘ finds both the ESC press release and the EurekAlert version. The EurekAlert one has had the reference trimmed off, but it’s in the original. So now I can search on the title of the research paper or, more reliably in theory, on the DOI permanent identifier. These lead to an error page at Oxford University Press saying
Sorry, the International Journal of Epidemiology content that you are trying to access has moved. Please search for the content using the DOI, Author or Title.
That advice does not get me any further. Neither does going in via the PubMed database. Looking further at the journal website, the paper is not in the ‘coming soon’ list, nor in any recent issue of the journal.
I have no idea what’s happened to the paper, but the Google does reveal a presentation about the research (PDF). I’m going to show you a graph from page 8.
They’re estimating a lower risk for people with children than those without. Among those with kids, the risk was higher with more, but by less than 5% per extra child.
As the researchers say, this probably isn’t biochemical, it’s probably socioeconomic. In which case, a cohort from China during both their economic boom and the One Child policy might not generalise all that well to New Zealand. And while I wouldn’t expect a busy journalist to go to the lengths I did to find a source, they should at least notice they don’t have one.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »