How do we know whether screening for something like cervical cancer is effective at saving women's lives? Two ongoing studies conducted in India (one funded by the National Cancer Institute and the other by The Gates Foundation) are aimed at answering that question — but their methods are under fire by critics.
It works like this. Say you want to test the effectiveness of a new screening method. You recruit a large group of women and you split them into two groups. One group gets the screening regularly. The other, the control group, doesn't get the screening. Then you follow them over time and track how many women in both groups died of cancer. That's a pretty basic scientific method. It's also something that prompts big questions about the treatment of women in the control group.
The people conducting the study say women in the control group were told they could seek out screening on their own. Critics argue that point (and the way the study worked) wasn't clearly explained, and that those alterante options weren't as available to the women as researchers imply. The majority of the women participating in the studies are poor and have very little formal education.
There are some important differences between this and the infamous Tuskegee syphilis experiment. In that case, researchers identified men with syphilis and neither told them about their disease nor offered them treatment — just monitored the deadly disease's progress. Here, there's clearly an attempt (however poorly executed) at being open with the women about what the study is and what is being done. Read the rest
Editorial note — Cow Week is a tongue-in-cheek look at risk analysis and why we fear the things we fear. It is inspired by the Discovery Channel's Shark Week, the popularity of which is largely driven by the public's fascination with and fear of sharks. Turns out, cows kill more people every year than sharks do. Each day, I will post about a cow-related death, and add to it some information about the bigger picture.
In 2009 and again in 2011, Welsh cattle joined forces to surround and kill women who were out walking their dogs on the outskirts of Cardiff. Apparently, cows really do not like it when you bring a dog around them. So, FYI on that. This story is from a survivor of the 2009 attack:
"I was slightly ahead when I saw the cows, they looked up and seemed curious and started to move towards us both," she said.
"They were coming in a semi-circular formation so I was heading towards the end so I could get away from them."
The next time she looked around Ms Hinchey appeared to be surrounded by the cows, she said.
One of things that made me post this particular story was the disconnect between the idealized image of a field full of docile cattle, happily grazing on grass ... and the truly creepy and threatening image presented in the quote above. I mean, it's like something from a Stephen King novel. Of course, I also don't have a lot of experience with cows in my personal, daily life. Read the rest
Paul Douglas is a Minneapolis/St.Paul meteorologist. Meteorologists don't study the same things as climate scientists—remember, weather and climate are different things—but Douglas is a meteorologist who has taken the time to look at research published by climate scientists and listen to their expertise. Combined with the patterns he's seen in weather, that information has led Douglas to accept that climate change is real, and that it's something we need to be addressing.
Paul Douglas is also a conservative. In a recent guest blog post on Climate Progress, he explains why climate isn't (or, anyway, shouldn't be) a matter of political identity. We'll get back to that, but first I want to call attention to a really great analogy that Douglas uses to explain weather, climate, and the relationship between the two.
You can’t point to any one weather extreme and say “that’s climate change”. But a warmer atmosphere loads the dice, increasing the potential for historic spikes in temperature and more frequent and bizarre weather extremes. You can’t prove that any one of Barry Bond’s 762 home runs was sparked by (alleged) steroid use. But it did increase his “base state,” raising the overall odds of hitting a home run.
Mr. Douglas, I'm going to be stealing that analogy. (Don't worry, I credit!)
A few weeks ago, I linked you to the introduction from my new book, Before the Lights Go Out, where I argue that there are reasons for people to care about energy, even if they don't believe in climate change—and that we need to use those points of overlap to start making energy changes that everyone can agree on, even if we all don't agree on why we're changing. Read the rest
Here are two myths you need to let go of:
The solution to high gas prices is more oil.
Climate change is something that happens to polar bears and people from Kiribati.
The truth is that fossil fuels are extremely useful and valuable. And, by their very nature, the supplies are limited. Likewise, climate change isn't just something that's going happen—it's already taking place, and you can see the effects in your own backyard.
Too often, I think, we talk about the risks of fossil fuel dependence and climate change in ways that make them seem abstract to the very people who use the most fossil fuels and create the most greenhouse gases. That's a problem. There are lots of reasons to care about energy. But I think that fossil fuel limits and climate change are the most pressing reasons. And I think it's incredibly important to discuss those very real risks in a way that actually feels very real.
This isn't about morality, or lifestyle choices, or maintaining populations of cute, fuzzy animals. (Or, rather, it's not just about those things.) Instead, we have to consider what will happen to us and how much money we will have to spend if we choose to do nothing to change the way we make and use energy.
Over at Scientific American, you can read an excerpt from my upcoming book, Before the Lights Go Out. In it, you'll read about the energy risks hanging over the Kansas City metro area—a place that, in many ways, resembles the places and lifestyles shared by a majority of Americans. Read the rest
A diagnosis of brain cancer is basically a death sentence. It's a terrible thing for anyone to deal with, and it's only made worse by all the uncertainty. Doctors don't really understand how brain cancer works very well. Beyond death, there's often not a lot that they can tell patients about what to expect—how the cancer will affect the brain, how fast it will spread, where it will spread to.
Eric Kostelich is one of the researchers who is trying to change that, by approaching the problem of brain cancer from a new angle. Kostelich is a mathematician. In particular, he's interested in how we can use math to better predict the behavior of complex and chaotic systems. Right now, this mostly means that he studies the weather. In fact, he's part of a team that developed a new algorithm for weather prediction, called the Local Ensemble Transform Kalman Filter. But Kostelich thinks that the LETKF could have applications outside the nightly news.
In a recent study, published December 21 in Biology Direct, he joined forces with cancer researchers, to see whether the statistical methods that make chaotic weather patterns more predictable could do the same thing for chaotic behavior in cancer cells. The results are promising. A couple of weeks ago, I spoke to Kostelich to find out more about the history of forecasting uncertainty, how algorithms like LETKF work, and what we might learn if we apply these systems to cancer.
Maggie Koerth-Baker: When you set out to apply the methods used to forecast the weather to cancer, why did you choose brain cancer? Read the rest
Right now, I'm reading a book about why catastrophic technological failures happen and what, if anything, we can actually do about them. It's called Normal Accidents by Charles Perrow, a Yale sociologist.
I've not finished this book yet, but I've gotten far enough into it that I think I get Perrow's basic thesis. (People with more Perrow-reading experience, feel free to correct me, here.) Essentially, it's this: When there is inherent risk in using a technology, we try to build systems that take into account obvious, single-point failures and prevent them. The more single-point failures we try to prevent through system design, however, the more complex the systems become. Eventually, you have a system where the interactions between different fail-safes can, ironically, cause bigger failures that are harder to predict, and harder to spot as they're happening. Because of this, we have to make our decisions about technology from the position that we can never, truly, make technology risk-free.
I couldn't help think of Charles Perrow this morning, while reading Popular Mechanics' gripping account of what really happened on Air France 447, the jetliner that plunged into the Atlantic Ocean in the summer of 2009.
As writer Jeff Wise works his way through the transcript of the doomed plane's cockpit voice recorder, what we see, on the surface, looks like human error. Dumb pilots. But there's more going on than that. That's one of the other things I'm picking up from Perrow. What we call human error is often a mixture of simple mistakes, and the confusion inherent in working with complex systems. Read the rest
I've talked here before about how difficult it is to attribute any individual climactic catastrophe to climate change, particularly in the short term. Patterns and trends can be said to link to a rise in global temperature, which is linked to a rise in greenhouse gas concentrations in the atmosphere. But a heatwave, or a tornado, or a flood? How can you say which would have happened without a rising global temperature, and which wouldn't?
Some German researchers are trying to make that process a little easier, using a computer model and a whole lot of probability power. They published a paper about this method recently, using their system to estimate an 80% likelihood that the 2010 Russian heatwave was the result of climate change. Wired's Brandon Keim explains how the system works:
Read the rest
The new method, described by Rahmstorf and Potsdam geophysicist Dim Coumou in an Oct. 25 Proceedings of the National Academy of Sciences study, relies on a computational approach called Monte Carlo modeling. Named for that city’s famous casinos, it’s a tool for investigating tricky, probabilistic processes involving both defined and random influences: Make a model, run it enough times, and trends emerge.
“If you roll dice only once, it doesn’t tell you anything about probabilities,” said Rahmstorf. “Roll them 100,000 times, and afterwards I can say, on average, how many times I’ll roll a six.”
Rahmstorf and Comou’s “dice” were a simulation made from a century of average July temperatures in Moscow. These provided a baseline temperature trend.