Monday, March 30, 2015

Christie Aschwanden's "The Case Against Early Cancer Detection"

I love counterintuitive data that challenges commonly held beliefs. And there is a lot of counterintuitive health data out there (For example, data questioning the health benefits associated with taking vitamins or data that lead to a revolution in how we put our babies to sleep AND cut incidents of SIDS in half).

This story by Aschwanden for discusses efficacy data for various kinds of cancer screening. Short version of this article: Early cancer screening detects non-cancerous lumps and abnormalities in the human body, which in turn leads to additional and evasive tests and procedures in order to ensure that an individual really is cancer free or to remove growths that are not life-threatening (but expose an individual to all the risks associated with surgery).

Specific Examples:

1) Diagnosis of thyroid cancer in South Korea has increased. Because it is being tested for more often. However, death due to thyroid cancer has NOT increased (see figure below). As such, all of the extra detection hasn't actually decreased mortality, but it probably has increased more evasive screening measures and surgery on people who will not die of cancer.

2) The number of false positives for cancer resulting from breast cancer screenings. While women are told to do monthly breast exams at home/annual screenings with the gynecologist, most of the lumps detected are innocuous, but women are exposed to radiation and biopsies in order to confirm this.

This article is a good example for:
1) Type I Errors
2) Absolute versus relative risk
3) How should data be used to prioritize health spending?
4) Would your students refuse preventive screenings after seeing data like this? Why or why not? What happens when intuition battles data?

Monday, March 23, 2015

Izadi's "Tweets can better predict heart disease rates than income, smoking and diabetes, study finds"

Elahe Izadi, writing for the Washington Post, did a report on this article by Eichstaedt et. al, (2015). The original research analyzed tweet content for hostility and noted the location of the tweet. Data analysis found a positive correlation between regions with lots of angry tweets and the likelihood of dying from a heart attack.

The authors of the study note that the median age of Twitter users is below that of the general population in the United States. Additionally, they did not use a within-subject research design. Instead, they argue that patterns in hostility in tweets reflect on underlying hostility of a given region.

An excellent example of data mining, health psychology, aggression, research design, etc. Also, another example of using Twitter, specifically, in order to engage in public health research (see this previous post detailing efforts to use Twitter to close down unsafe restaurants).

Thursday, March 19, 2015

Harry Enten's "Has the snow finally stopped?"

This article and figure from Harry Enten (reporting for fivethrityegiht) provides informative and horrifying data on the median last day of measurable snow in different cities in America. (Personally, I find it horrifying because my median last day of measurable snow isn't until early April). This article provides easy-to-understand examples of percentiles, interquartile range, use of archival data, and median.

Portland and Dallas can go suck an egg.