In 1994, the World Health Organization started measuring the cumulative healthy years lost to disease with Disability Adjusted Life Years (DALY). And each infectious disease is currently ranked according to its DALY score, providing a numbered system to help guide the public health community in crafting a suitable approach to managing the myriad of diseases they face.

But a group of European researchers want to flip the system on its head. Their new method, instead of relying on observations and statistics calculated over the course of months to years, prioritizes infectious disease based on a quick search of scientific and medical publications. And according to a study they published in PLoS ONE, not only is their way faster, but it’s comparably accurate to WHO in identifying the latest trends.

The Hirsch index (or h-index) was created as a way to measure the impact a particular scientist has on his or her field. The value, h, represents the number of publications the researcher has with at least h other papers citing those works. So a scientist with an h-index of 10 has published that many journal articles which have been cited by 10 times a piece by others. It gives a reliable measure of a researcher’s overall value – and, let’s face it, feeds their ego better than standard measures of success, like publishing in top-tier journals.

By searching certain pathogen keyword terms, the team was able to adapt the h-index to score the impact of infectious disease. And as shown below, the two methods produced similar results. But the coolest part is that a single researcher complied h-indices for ~1,400 pathogens in 2 weeks’ time.

Granted, there are some limitations to the method described above, since no distinction is made between good and bad research articles, and emerging diseases may be ignored since they haven’t had enough time to generate a significant h-index score. But as I’ve discussed before, there is a clear trend of people working to bring public health data to light faster than before. It’ll be an interesting space to watch in the next few years.

When public health officials track the outbreak of a virus, like H1N1, it takes time to get the story right. They have to collect and assemble data from institutions scattered across the country, a process that can be, well, slow.

For instance, at the CDC’s FluView website, you can see statistics for influenza trends across the country. But today’s “weekly influenza report” was assembled with data from the week ending 7 May 2011. Or put another way, the latest information is already 11 days old.

It seems crazy that sometimes the information we desperately need is the most difficult to get, but it’s all too often true. You can up-to-the-minute details on the location of your neighborhood’s taco truck, but if you want flu data, you’ll have to wait about 2 weeks.

The difference, of course, is that the food trucks have wholeheartedly embraced social media, which has quickened the pace of information flow. And as more and more people are using services like Twitter – which in 2010 was growing at a rate of 300,000 users each day – a savvy group of researchers from the University of Iowa wondered: if people are using Twitter to catalogue the minutia of their lives, could the tweets be analyzed to better track outbreaks of the flu?

Starting in April 2009, the research team led by Philip Polgreen, an assistant professor in the Department of Internal Medicine, starting logging tweets from users living in the US, and combed thru the data, filtering for certain keywords, like flu, swine, influenza, vaccine, H1N1, Tamiflu, etc.

The first thing they noticed was that the general Twitter chatter about H1N1 peaked before the outbreak surfaced (check out the figure above). The red line represents the percentage of tweets talking about the flu or flu-like illness, while the green line shows the number of confirmed or probable cases. Whether this reflects an ability of social media to “predict” an outbreak remains unclear. But one thing’s certain: people were aware of the storm that was brewing.

According to the study, in early May 2009, the CDC released targeted messaging to consumers about the importance of flu prevention. So when the team searched through the Twitter data for specific phrases like “mask” or “hand hygiene” they were able to gauge how prevention strategies were rippling through the virtual community. [Notice the two distinct peaks in Twitter traffic for "mask" (green line) and "hand hygiene" (red line) in the figure above.]

Seeing how Twitter chatter of certain keywords, however interesting it may be, doesn’t do anything to address the larger problem, which is: How many people are infected with the flu at this very point in time? So the team devised a complicated statistical model to estimate the number of people infected with the flu based on their Twitter status. And surprisingly, when they compared their numbers (red line in figure below) to the count generated by the CDC (green line), they discovered the data were indistinguishable. However, they would have had the current estimates in hand a lot sooner than the CDC.

The authors acknowledge that their model needs to be validated by others. So consider this finding exactly what it is, a solid first step in a lengthy journey.

Citation: Signorini A, Segre AM, Polgreen PM, 2011 The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLoS ONE 6(5): e19467. doi:10.1371/journal.pone.0019467

My latest story for Wired Playbook discusses recent research from a group that analyzed 46 seasons of professional German soccer league data to determine that firing a coach mid-season — a tactic clubhouses use to jump-start a fledgling team — has absolutely no effect on the squad’s performance.

So, to really compare apples to apples and provide a clearer picture of what effect a new coach has on a losing team, Heuer thought it better to identify suitable control groups — teams that had bad luck, but stuck it out with their current coach for the rest of the season — and compare them to teams that handed their coach a pink slip when times got tough.

As they suspected, there was absolutely no difference between the teams that fired or retained their coach, as all teams that experienced an early period of bad luck showed improvement later in the season. But pride is a formidable enemy, and the data consistently showed that in many cases, a team decided to prematurely give their coach the boot after they took a beating on two consecutive games.

Read the full story here.

Photo via Flickr / BrokenRhino

Heuer, A., Müller, C., Rubner, O., Hagemann, N., & Strauss, B. (2011). Usefulness of Dismissing and Changing the Coach in Professional Soccer PLoS ONE, 6 (3) DOI: 10.1371/journal.pone.0017664

I’m a little late posting this one here, but last month I wrote a story for Wired Playbook on how athletes, much like musicians, seem to have brains that are beefier in certain areas.

Instead of just comparing the brains of athletes to non-athletes — a correlation that wouldn’t necessarily show if sports causes the brain to gain mass or if people with a thicker cortex in these areas are more likely to excel in athletic competition in the first place — the researchers determined how each year of practice correlated to changes in the brain:

However, in one of the brain areas studied, the researchers found that the number of years each athlete competed as a diver nearly predicted how thick the subject’s brain would be. If the results of this small study hold, there may be some biological truth to the adage, “practice makes perfect.” It’s as if each year of sports experience becomes neatly folded as a new layer of neurons atop previously mastered skills, physical knowledge, and competition know-how that have already been crammed into the brain.

I think it’s interesting to think about how these findings could impact sports statistics in the future. I mused:

These findings provide a small glimpse of how biometric and neurological data may one day be used to gauge a player’s ability and performance. Granted, there’s still a lot of work to be done in understanding exactly what’s going on in an athlete’s head.

Read the entire story here.

Photo via Flickr / alandberning

Wei, G., Zhang, Y., Jiang, T., & Luo, J. (2011). Increased Cortical Thickness in Sports Experts: A Comparison of Diving Players with the Controls PLoS ONE, 6 (2) DOI: 10.1371/journal.pone.0017112

Sad statistics, laid out in a provoking article from The Atlantic.

Despite sitting on a trust fund that’s worth over $1 billion in equity from a “purchase” of the Black Hills that the tribe never agreed to, the Sioux are suffering from chronic disease and have what’s sure to be one of the lowest ethnic life expectancies in the United States:

According to Oglala President John Yellow Bird Steele, almost half of Oglala Sioux over 40 have diabetes, and in the Western Hemisphere, few countries have shorter life expectancies (for men it is 48; for women, 52).

Photo via Flickr / cm195902

First, I came across some interesting statistics that I wanted to share (from Science Progress):

19.8 percent of adults in the United States (43.4 million people) were current smokers in 2007.

30 percent of all cancer deaths involve smoking as the primary cause.

443,000 people died prematurely every year as a result of smoking and exposure to tobacco smoke during the period between 2000 and 2004.

During that same period, smoking caused $98 billion in productivity losses each year.

For every person who dies of a smoking-related disease, 20 more people suffer with at least one serious illness from smoking.

20 percent of high school students were smokers in 2007.

3,600 people between the ages of 12 and 17 pick up smoking everyday.

I also found an interesting study that discussed the paradox of nicotine use: Users are thin and have low body fat, but are at an increased risk of cardiovascular disease.  So what is it in cigarettes/nicotine that’s causing heart problems?  A research group at Charles Drew University investigated the effects of giving nicotine to mice.  Although the mice lost weight and ate less than the control animals, the nicotine-fed mice developed insulin resistance, which is a precursor to diabetes, and may explain the increased development of heart disease in nicotine users.

Franklin Roosevelt's blood pressure chart for 1944

One of the key components of making the right health decisions is – and ever will be – having the right information from which to decide. In today’s world of blood tests and screening exams and Gleason scores, this seems pedestrian. But the fact is that medicine only began quantifying health in the early 1900s, with the notion of high blood pressure, and it was well into the 1950s before individuals became aware of their numbers. I read recently that FDR’s blood pressure was high for nearly a decade, hovering as high as 200/150- astronomical, by today’s standards -for years, and was locked at 260/150 near his death from, yup, heart disease. But with no treatment available, the number was simply a warning that, maybe, he should cut back on smoking a bit.

In the 60 years since, the number of commonly tracked health metrics has soared, so much so that, these days, you can track them on your iPhone

The ability to track (and utility of tracking) these metrics seems to me increasingly important. While my colleagues over the Quantified Self have been sniffing around the greater landscape of personal metrics (UPDATE: and Alexandra Carmichael recently posted the 40 things about herself that she tracks daily), from productivity apps to those photo-a-day guys, I’ve been especially interested in those metrics that we can use to provide feedback and can perhaps manipulate in the hopes of improving our health (whether it’s running faster or weighing less). Feedback, to me, is key. Where FDR could only watch his numbers climb, now to have our numbers is to have the opportunity to adjust our numbers.

Which brings me to the point of this post: Aan effort to begin cataloging all the health metrics ordinary citizens might have available to track. The list – which needs your help – begins after the jump:

I’ve divided these into three categories (for now). Basic stats, Biometrics (in the sense of physiological statistics), and Relative Stats (variable inputs & subjective data). There may be better categories, and there are certainly stats I’m missing, so please help me add more

Basic stats:

• Height
• Weight
• Sex
• Age

Biometrics:

• Blood pressure
• Cholesterol count (LDL and HDL)
• Menstrual cycle (time)
• Blood glucose level
• liver enzyme level
• Gleason score (prostate test)
• (lots more blood tests out there)

Relative Stats:

• calorie intake
• fat intake
• transfat intake
• protein intake
• carb intake
• exercise (time, weight, reps)
• mood