As part of ongoing efforts to support MOOCs (massive open online courses), we’re closing our traditional EpiAnalysis blog and converting to a new, evolving epidemiology virtual course. While our older posts will remain available on this website, our newer effort will allow us to more flexibly discuss ongoing epidemiology controversies, new research and data, and analytical approaches. Stay tuned…
We know health starts—long before illness—in our homes, communities, schools and jobs. But we devote most attention to medications and healthcare delivery. The Stanford Prevention Research Center is announcing the start of a new “Health 4 America” fellows program, whose goal is to train prevention experts to address health in families, neighborhoods, schools, communities and the workplace…
Today’s PLoS Medicine includes our recent study attempting to answer a simple question: given the rise in many chronic disease risk factors (high blood pressure, cholesterol, diabetes, etc.) in rapidly-developing countries like India and China, which interventions might avert the most deaths from cardiovascular disease?
This is a guest post by the computational epidemiologist Dr. John Ayers:
Most of us are aware of the “big data” revolution fueled by electronic information. It has been suggested that big data, along with hypothesis-free methods popularized by films such as Moneyball, will allow for an unprecedented growth of knowledge across disciplines, including epidemiology and preventive medicine. While I am a bit more circumspect in expectations (there is no substitute for survey data in many cases), I do believe that electronic data collected for a fraction of the cost of survey data can work hand-in-hand with research derived from more traditional sources.