Health Awareness Days: Sufficient Evidence to Support the Craze? A New Commentary in AJPH

My regular readers know that I have some hesitations about campaigns that focus on "raising awareness". The phrase is vague and its definition often varies depending on who you ask.

So I'm excited to share that I recently published a new commentary on the topic with my colleague Dr. Jonathan Purtle at the Drexel University School of Public Health. "Health Awareness Days: Sufficient Evidence to Support the Craze?" was published by the American Journal of Public Health yesterday. Drexel University posted a great press release that summarizes the article and includes comments from us regarding recommendations and next steps.

I hope you will all read and share the commentary!

Using Twitter to Track Disease: Weighing the Advantages and Challenges

A few weeks ago I participated in a fantastic twitter chat on the use of social media for public health.  During the event, our moderators posed the following question: "Are there any other diseases (besides the flu) that we could track on social media?"

The question generated a very lively discussion that I was inspired to revisit on Storify this morning after reading the Washington Post's article, "Twitter becomes a tool for tracking flu epidemics and other public health issues." 

The WP article highlights several advantages and challenges of monitoring public health diseases and/or conditions on twitter.  My twitter chat colleagues brought up many other important issues for us to consider, so I'm including these expanded lists:

Advantages:

• Offers real time data on health or behavior (Government data can often take weeks or months to be released)
• There is so much available data! 
• It could capture cases that would not otherwise be formally documented at a physician's office or hospital.
• It has proved helpful in tracking time sensitive disease outbreaks (e.g., Novovirus).  *Check out this article about how twitter was used to track Norovirus activity during a journalism conference.

Challenges:

• Accuracy and case definition (i.e., does a twitter user really have the flu or just a bad cold?)
• Tracking specific words like "sick" or "flu" can bring up a lot of content that is unrelated to the twitter user being ill themselves (e.g., "I'm so sick of this terrible weather").  *Check out how Johns Hopkins researchers are working to address this problem by better screening tweets.
• We must differentiate between tracking symptoms vs. tracking cases- they are not the same.
• Our search strategies should include various terms or slang that are used to describe the disease or behavior of interest.
• Caution: media coverage of certain illnesses can cause a spike in key words on twitter without a rise in actual cases.
• What are the privacy concerns?
• Twitter might not thoroughly capture diseases or conditions that carry stigma (e.g., mental illness) because users may be hesitant to discuss them in a public forum.
• Results could be skewed by populations who are over or under represented on twitter.
• Do we need to train "trackers" to intervene? E.g., what if they are monitoring dangerous tweets/behaviors like suicidal ideation and attempts?

While the challenges list is quite long, I hope we are not discouraged!  I think twitter is an enormous resource for public health professionals.  We just need to be thoughtful and thorough regarding how to use twitter effectively.

More Resources:

The Washington Post article and related stories shared great links to more information about research in this area:

• YouTube video on University of Rochester efforts to track influenza on twitter.  It also describes their related app: Germ Tracker (warning: it may have you hopping off your regular morning bus). 
• Johns Hopkins University article: "You Are What You Tweet: Analyzing Twitter for Public Health".
• Brigham Young University article: "'Right Time, Right Place' Health Communication on Twitter: Value and Accuracy of Location Information".
• A great article that highlights what we can learn from Google Flu (since their predictions were off this year)- emphasizes the importance of "re-calibrating" your models or algorithms each year.

What Do You Think?

• What other advantages and/or challenges should we add to the list?
• What other resources can you share?