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

*Note: I’ve been using Google+ for a few weeks now, mostly as an intermediary between sharing a link on Twitter and writing a blog post. For the time being, I’m going to repost the content that generated a lot of interest. -BJM

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.

If the idea of triaging patients at the emergency room seems complicated, consider how public health officials prioritize threats posed by organisms they can’t even see. Yet the microscopic microbes and viruses that sicken millions of people with infectious diseases still require a plan of attack. As in any medical scenario, resources are limited. And whether it’s due to low staff numbers, not enough research dollars, or too few hours in the day, someone ultimately has to make the call on where to funnel assets.

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.

Read more…

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.

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One morning, a little over a year ago, I woke up with a very sore, and slightly swollen elbow. I remembered that I had cut my arm on a neighborhood bar table while watching a football game with some friends a few days prior, and I wondered if the cut was infected. I made an appointment with my primary care physician, who quickly diagnosed me with bursitis, an inflammation of the fluid-filled sac that pads the elbow. Since I had broken skin, the doctor wisely prescribed clindamycin, an antibiotic, to treat any tissue infection that may have seeped in.

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.

Read more…

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.

The New York Times had a story on Friday criticizing the National Cancer Institute for its new decision tool on colon and rectal cancers. The problem, the reporter says, is that the tool – an interactive questionaire that creates a risk estimate for developing colon cancer – only works for white people. African-Americans or Hispanics who try the tool get a message that says: “At this time the risk calculations and results provided by this tool are only accurate for non-Hispanic white men and women ages 50 to 85.”

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.