Author Archives: Niko Yiannakoulias

Immunization and Web 2.0

We just published a new article related to vaccine content on YouTube:,60n7a2G8

This research started as part of an honours thesis by Monika Chase, an undergraduate student in the School of Interdisciplinary Science. We expanded this work and turned it into a paper exploring vaccine related content on YouTube.

Our objective was to compare sentiment (measured by views and likes) and word choice (based on automated transcripts of videos) across measles and influenza immunization-related content.

We found some slight differences between different ‘flu’ and ‘measles’ videos, but found some other interesting things as well. For example, there is a spike in the volume and ‘likeability’ of measles content around the time that measles outbreaks occur.

We also found that while anti-immunization videos use the language of science, they contain slight differences that may make them easy to detect for surveillance systems. This could be useful for detecting trends in anti-immunization content in social media.

The Decision Game

Here is a link to a paper (published in the Journal of Risk Research) using the Decision Game that I wrote with two graduate students, Julien Gordon (who lead the development and design of the game) and Connor Darlington.  This paper is mostly about the dynamics of the game, and makes a comparison between online and face-to-face play.  Julien is finished his thesis, and is writing up his findings in another paper that will be submitted for publication very soon.

Regionalization in R

Last spring I completed a small project to write a regionalization/political districting program in R.  I never really did anything with it because it’s pretty slow.  It is very much a work in progress, however I release it to the world here:

The main program is called reRegionalize.R.  It calls the other code, so all of these should be unzipped into the same folder.  It works with an example data set, but it’s fairly easy to use your own.

If I start working on it again, I will post it on githuband update the link.  Use at your own risk.

A halloween injury epidemiology nightmare — of mine!


This post includes a mistake I made, which was caught by the authors of the paper.  I think this happened because I conflated the information reported in the media with the information in their paper.  According to the authors, the relative risk of youth injury (0-17 in age) is not a 43% increased risk on Halloween, but a 360% increased risk.  This means that the paper’s estimated attributable risk is about 10 times higher than I calculated. The correct interpretation is that once every 3 years, a Canadian will die due to Halloween.

Nevertheless, I stand by my conclusion; we are not serving the public interest by using only relative risk to communicate health risks to the public.

A paper (‘research letter’, actually) was recently published in JAMA pediatrics  (‘Pedestrian Fatalities Associated With Halloween in the United States‘) which attempts to quantify the impact of Halloween on child pedestrian fatality risk in the United States.  I have looked at the original study, and using the numbers reported in  the study, along with a few other numbers, I have calculated the actual impact of Halloween on the risk of child pedestrian fatality in Canada.  I used Canada as a reference, but the general ideas here would be roughly the same as in the U.S..

Here is the screenshot of the results of my analysis:

And here is a link to the Google Sheet where I do all the calculations:

The links in the document take you to the sources of data I used for these calculations.

What does it mean?

The authors of the study show that the risk of a child dying as a pedestrian as a result of a motor vehicle collision is 1.43 times higher on Halloween than on the days immediately before and after Halloween.  The main reason is probably exposure–more children walk at night on Halloween than on other days.  I don’t doubt their findings–the methods seem reasonable.

What I object to is how these results have been framed, particularly by the media.  The researchers and media focus on the measure of relative risk–that Halloween is associated with a 43% increased risk of child pedestrian fatality.  However, we need to put these results in proper perspective.  Given how rare child pedestrian fatalities are, the actual impact of this increased in risk on the population is very small.  As you can see above, in Canada, we should expect roughly 1 extra child pedestrian death every 30 years due to this Halloween effect.

Think of the children!

Some might argue that even one extra child pedestrian death is one too many.  That seems reasonable on the face of it, but it’s also naive.  Life involves making trade-offs.  While we could go out of our way to increase policing on Halloween, inform and educate parents of risks, add street lighting, and end trick or treating altogether, all of these risks come with a cost, and there’s no guarantee that any of them would even reduce the fatality risk at all.  Furthermore, there are probably more cost-effective ways of saving children’s lives–such as increasing immunization rates, particularly in the developing world.

Finally, media stories about the dangers of Halloween have an important social cost.  They add to the culture of fear, paranoia and helicopter parenting that threaten to further erode the joyful chaos of childhood.  This exceedingly small risk may be real, but is it really worth the attention it received on Halloween given the impact it may have on parental attitudes towards safety in their community?

My relative risk diatribe (again!)

This is another instance of media sources putting emphasis on relative rather than attributable risk.  In fairness, the researchers do not discuss attributable risk in their paper, and so perhaps it’s unfair to blame non-expert journalists for not figuring it out on their own.  Relative risk provides little useful context for understanding the impact of rare events.  Relative risk tells us only the difference in risk between exposed and non-exposed groups; it does not tell us about the actual risk in our lives.  A relative risk of mortality of 2.0 (where exposure increases risk of death by 100%) sounds terrifying on the face of it, but what if the baseline risk is one in a billion?  This would mean risk from exposure would go from from 0.000000001 in the unexposed to 0.000000002 in the exposed, and result in one extra death per billion people due to exposure.

In terms of relative risk, Halloween seems pretty terrifying to child pedestrians; however, relative risk does not tell us a complete picture, since pedestrian fatalities are (fortunately) rare and Halloween only happens once a year.  In terms of attributable risk and actual impact on the population’s health, the impact of Halloween on pedestrian safety is pretty small, and it’s not clear that knowing about these risks (when measured in terms of relative risk) is meaningful, particularly since there are material and social consequences to fear associated with media stories about the dangers of our world.