The idea that climate change is linked to the spread of a disease is not new. Some bacteria and viruses, after all, piggyback on an animal or insect, and the infectious advance depends on the host’s reaction to climbing temperatures. Consider dengue, a disease once anchored to tropical climates by its host’s penchant for heat and humidity, which is now pushing further north with its mosquito transits as the upper latitudes get warmer. But according to a study published this past June in PNAS, it’s not only climbing temperatures that are worrisome; in the past, even heavy rains have altered the course of disease, though often in divergent directions.

During the third plague pandemic (China, 1850-1964), researchers found that, for better or worse, the seasonal rains were a strong predictor of how the disease spread. There, storms governed Pestilence’s toll, prodding the disease in the arid north, and quelling it in the humid south.

Rats are the primary host for the bubonic plague, and in general, the more that infected rats move, the more the disease will spread. In the dry north, they figure, the rains quenched the parched landscape, causing the rats, and the disease, to stir. In the southern part of the country, the rains only served to make the humidity worse, perhaps forcing the rats to sit tight.

Keeping tabs on the spread of infectious disease is one thing; understanding the interaction of pathogens, hosts, and behavior is yet another.

Photo via Flickr / Yorick_R

GOOD has an interesting infographic on deadly disease outbreaks throughout history. Though measles and smallpox are the most prolific microscopic assassins, claiming over 500 million lives, these diseases have been around forever — measles since the 7th century BC, smallpox since 10,000 BC.

What’s more surprising to me is that the Spanish Flu killed up to 100 million people in just over a year’s time as the 1918 flu epidemic spread.

Read the post on GOOD here.

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

As the hours crept by, the pain in my elbow worsened, until I woke up in the middle of the night with extreme arm pain. I immediately checked the elbow that had been swollen the previous day. The swelling had doubled in size, and the skin was an angry-red color. The following morning, I was back in the clinic, and my doctor started to suspect that this was no ordinary infection on my elbow, and may in fact be a drug-resistant staph infection. Gulp. Nonetheless, he felt confident that the clindamycin should clear it up.

Under the doctor’s orders, I spent the next day meticulously tracing the swollen area on my elbow with a Sharpie marker, carefully noting how much it spread. By the end of the day, my entire forearm was puffy and discolored, and my doctor said it was time for me to be admitted to the hospital. I spent 3 days there, getting intravenous treatments of vein-burning, gastrointestinal-rearranging Vancomycin pumped into my system. Not fun.

Afterward, I talked to a number of physician friends about my experience. They said my doctor’s treatment plan was textbook. He had done everything right. When docs suspect drug-resistant staph, the first line of defense is typically a hearty dose of clindamycin. The problem in my case was that the staph I contracted was actually resistant to clindamycin. That explains why the infection continued to spread even though I was taking the antibiotics.

Since this little microbial foray, I’ve had a growing interest in infectious disease. Specifically, I like seeing smart, new ways to keep tabs on how bacterium move from place to place. I wonder, if my doctor had known that clindamycin-resistant staph was infiltrating San Francisco, would I have initially received a different antibiotic? In my opinion, this was a clear case where having more data would have aided the diagnosis, and hastened a healthy outcome.

As Thomas pointed out at The Huffington Post, the true promise of personalized medicine is more about data than specialty drugs. Data can be our personal metrics, such as blood pressure, glucose levels, or cholesterol values. But keeping medical data to ourselves would be somewhat shortsighted. The internet has taught us the power of sharing data. We share our photos on Flickr. We share our status messages on Facebook. We share links on Twitter. Likewise, we can share our health and medical data, enabling pooled statistics from large populations. In the case of infectious disease, the best preventive strategy is to know exactly what strains you’re up against, and how the microbes are moving into different geographic regions over time.

Researchers recently confirmed the power of sharing microbial data in a new report, published this month in PLoS Medicine. Roughly 25% of us walk around with staph on our skin, yet not all of us get sick. That’s because there’s relatively few strains that cause serious symptoms. These so-called virulent strains are the ones docs want to track.

Following both methycilin-resistant (MRSA) and methycilin-susceptible (MSSA) staph strains through Europe, the authors coordinated the participation of 450 hospitals in 26 European countries, a logistic feat in its own right. When a case of staphylococcus aureus was found, the bacterium was genotyped (i.e. its DNA was analyzed to identify which strain it came from), and its location recorded. After collecting all the data, researchers could see how a particular strain of staph localized in different geographic regions. For instance, did the virulent strains stay in one hospital, or had they spread throughout the community?

The authors found that most virulent MRSA strains were contained in a health care clinic, meaning that drug-resistant staph was simply hopping from person-to-person within the hospital walls. Occasionally, that MRSA strain would show up at a different, nearby hospital, and rapidly spread in admitted patients. This implies that the carriers of the virulent MRSA strains are patients who are repeatedly admitted to different regional hospitals.

I’ll leave you with a final thought: tracking microbes isn’t just a task for researchers. In fact, DIYBio types should check out a cool new project called BioWeatherMap, which asks volunteers to swab commonly used public surfaces, such as door knobs or crosswalk buttons, to track pending microbial storm fronts.

A new Nature news story discusses the little known fact that there are two different types of adipose (fat) tissue: white and brown.  White fat tissue stores excess calories that are not used for energy as lipids, and typically accumulates around the hips and thighs of the girls, and around the belly of the guys.  Simply put, it’s the excess inches we try to get rid of through diet and exercise.  Brown adipose tissue (BAT), on the other hand, typically accumulates around the collarbone, shoulder blade, and neck area.  Originally thought to only be present in human newborns and animals, BAT is unique in that it burns excess fat calories, as opposed to storing them, to keep the body warm.

However, in recent studies published in The New England Journal of Medicine, researchers found metabolically active BAT in an unexpected place — on human adult volunteers.  The studies used Positron Emission Tomography (PET), which measures where consumed radio-labeled glucose is metabolized in the body.  Subjects were scanned either at room temperature, or in a cold room (17-19 deg Celsius), while their feet were repeatedly immersed in cold water (7-9 deg Celsius).  It turns out that with the cold room and ice-cold foot bath, there was a significant increase in the metabolic activity of the fat tissue around the collarbone and shoulder blades, compared to scans taken at room temperature.  Cold temperatures activate the sympathetic nervous system, and epinephrine (adrenaline) is released, which causes the body to warm itself.  These results show that in colder temperatures, calories may not be stored on your waist or hips, but rather, metabolized by the brown adipose tissue to keep you warm.

Despite their findings, it’s not suggested you take your lunch and head for the nearest walk-in freezer.  But the key finding is that BAT metabolism is triggered by adrenaline, the same hormone responsible for the “fight or flight” response.  Therefore, these results open the possibility that new drugs that activate the sympathetic nervous system to release adrenaline may be a viable treatment for obesity.

It’s an odd story for a couple reasons. First, the reporter seems to have created the controversy on her own – she quotes only one critic of the tool, and that critic is described as reacting negatively “after being referred to the site by a reporter.” The same reporter, I assume, who’s writing the story. Hmm.

But the real problem with the critique is that it barely acknowledges the reason that the tool only works for whites: It’s because the data on risk for colon cancer that’s built into the site is based on research that only studied whites. In other words, the NCI used the existing epidemiology, the data that exists, which is based on a Caucasian population study. More research is being done on risks in other populations, but it’s not substantial enough to merit a valid decision tool.

I’m all for calling on the NCI to extend the tool and fund the science that will relate to more people. But this is the way of all research – you take certain populations, which correlate in various ways to larger populations, and try to ascertain risk. Rare is the study that’s so well funded and so well managed that it can handle the full spectrum of people in the US. So the science evolves slowly, piece by piece, and over time the broader population is covered. Yes, there is such a thing as disparities in health research – certain populations are regrettably understudied. But there’s no indication in the Times story that that’s the case with colon cancer.

So the Times story has the effect of criticizing the NCI for creating a tool because it’s incomplete, entirely missing the forest for the trees: The great thing here is that such a tool exists in the first place. This is the sort of thing we should be encouraging the NCI and other health entities to do – show us the science, and show us how it’s relevant, *as it emerges and as soon as it emerges*. These sorts of risk assessment tools are incredibly powerful ways for individuals to think about their health. They help us understand the great body of science in immediately personal terms, giving us perspective on how our decisions – in this case, how much exercise we get or how many vegetables we eat – affect our risk for developing cancer. This should be applauded and encouraged, not criticized for failing to emerge in an all-at-once exhaustive form.

Indeed, the one critique that I have about the NCI’s tool – which you can see here – is that it doesn’t make plain how your risk stands up against other people’s, nor does it make plain what sort of changes could reduce your risk. When I played around with a worst-case scenario for me – only some exercise and not many vegetables – it gave me a lifetime risk of about 6%. But without the context of a general population, I have no idea if that’s high or low. And when I fiddle with the numbers and say I take aspirin and eat lots of vegetables and get lots of exercise, my lifetime risk drops to 1.6%. Much better, but I had to guess at what variables to change – meaning I have to guess at what changes to make to my life to improve my odds. If the NCI automated these functions and let me know where I stood and what I might consider changing, the tool would be a lot more potent.