5 Things to Know About Hot Drinks and Esophageal Cancer

Fun fact: according to CNN, on New Year’s Day 90% of the US never got above freezing.

Second fun fact: on my way in to work this morning I passed an enormous fire burning a couple hundred yards from where the train runs. I Googled it to see what was happened and discovered it was a gas main that caught on fire, and they realized that shutting the gas off (normal procedure I assume) would have made thousands of people in the area lose heat. With temps hitting -6F, they couldn’t justify the damage so they let the fire burn for two days while they figured out another way of putting it out.

In other words, it’s cooooooooooold out there.

With a record cold snap on our hands and the worst yet to come this weekend, I’ve been spending a lot of time warming up. This means a lot of hot tea and hot coffee have been consumed, which reminded me of a factoid I’d heard a few months ago but never looked in to. Someone had told me that drinking hot beverages was a risk factor for esophageal cancer, but when pressed they couldn’t tell me what was meant by “hot” or how big the risk was. I figured this was as good a time as any to look it up, though I was pretty sure nothing I read was going to change my behavior. Here’s what I found:

  1. Hot means HOT When I first heard the hot beverage/cancer link, my first thought was about my morning coffee. However, I probably don’t have to worry much. The official World Health Organization recommendation is to avoid drinking beverages that are over 149 degrees F. In case you’re curious, Starbucks typically servers coffee at 145-165 degrees, and most of us would wait for it to cool for a minute before we drank it.
  2. Temperature has a better correlation with cancer than beverage type So why was anyone looking at beverage temperature as a possibly carcinogen to begin with? Seems a little odd, right? Well it turns out most of these studies were done in part to rule out that it was the beverage itself that was causing cancer. For example, quite a few of the initial studies noted that people who drank traditional Yerba Mate had higher esophageal cancer rates than those who didn’t. The obvious hypothesis was that it was the Yerba Mate  itself that was causing cancer, but then they noted that repeated thermal injury due to scalding tea was also a possibility. By separating correlation and causation, it was determined that those who drink Yerba Mate (or coffee or other tea) at lower temperatures did not appear to have higher rates of esophageal cancer. Nice work guys.
  3. The risk has been noted in both directions So how big a risk are we looking at? A pretty sizable one actually. This article reports that hot tea drinkers are 8 times as likely to get esophageal cancer as those who drink tea at lower temperatures, and those who have esophageal cancer are twice as likely to say they drank their tea hot before they got cancer. When assessing risk, knowing both those numbers is important to establish a strong link.
  4. The incidence rate seems to be higher in countries that like their beverages hot It’s interesting to note that the US does not even come close to having the highest esophageal cancer rates in the world. Whereas our rate is about 4.2 per 100,000 people, countries like  Malawi have rates of 24.2 per 100,000 people. Many of the countries that have high rates have traditions of drinking scalding hot beverages, and it’s thought that combining that with other risk factors (smoking, alcohol consumption, poverty and poorly developed health care systems) could have a compounding effect. It’s not clear if scalding your throat is a risk in and of itself or if it just makes you more susceptible to other risks, but either way it doesn’t seem to help.
  5. There is an optimum drinking temperature According to this paper, to minimize your risk while maximizing your enjoyment, you should serve your hot beverages at exactly 136 degrees F. Of course a lot of that has to do with how quickly you’ll drink it and what the ambient temperature is. I was pretty impressed with my Contigo thermos for keeping my coffee pretty hot during my 1.5 mile walk from the train station in -3 degrees F this morning, but lesser travel mugs might have had a problem with that. Interestingly I couldn’t find a good calculator to track how fast your beverage will cool under various conditions, but if you find one send it my way!

Of course if you really want to cool a drink down quickly, just move to Fairbanks, Alaska and throw it in the air:

Stay warm everyone!

What Can Your Dentist Tell You About Your Cancer Risk?

Welcome to “From the Archives”, where I dig up old posts and see what’s changed in the years since I originally wrote them.

From time to time something fun reminds me of an old post of mine and I get all excited to go back and research what’s changed since I originally wrote them.

This is not one of those times.

A past post popped in to my head last week, but not for a good reason. A childhood friend of mine was diagnosed with ovarian cancer recently, which is a bit of a shock since she’s only 35, and hits close to home since she has a daughter just a bit younger than my son. Working at a cancer hospital I am unfortunately used to seeing early and unfair diagnoses, but it still has an extra sting when it’s someone you know and when they’re in the same phase of life you are. This friend actually has an interesting intersection with this blog, as she’s a science teacher  whose class I’ve visited and given a version of my Intro to Internet Science talk to. She does great work with those kids, and I loved meeting her class. If you’re the prayers/good thoughts type, send some her way.

Not the happiest of introductions, but the whole experience did remind me about how important it is for people to know the signs of ovarian cancer, as it can be easily missed. Additionally, it made me think of my 2013 post “What Can Your Dentist Tell You About Your Risk For Ovarian Cancer?” where I blogged about the link between congenitally missing teeth and ovarian cancer. I wondered if there had been any updates since then, and it looks like there are! Both scientifically and with a couple dozen spammers who left comments on my original post. Cosmetic dentistry folks apparently have a lot of bots working for them. Anyway, let’s take a look! At the science, not the spammers that is.

First, some background: For those of you who didn’t read the original post, it covered a study that found that women who have ovarian cancer are 8 times more likely to have congenitally missing teeth than women who don’t have ovarian cancer. Since I have quite a few congenitally (ie born that way not knocked out or pulled) missing teeth (both mandibular second molars and both mandibular second bicuspids), I was pretty interested in this fact. I used it as a good example of a correlation/causation issue, because there is likely a hidden third variable (like a gene mutation) causing both the missing teeth and the cancer as opposed to one of those two things causing the other one.

So why missing teeth? Well, first, because it’s kind of fascinating to think of tooth abnormalities being linked to your cancer risk. Dental medicine tends to be pretty separate from other types of medicine, so exploring possible overlaps feels pretty novel. When someone has teeth that fail to develop (also known as hypodontia or angenesis), it’s thought to be a sign of either an early developmental interruption or a gene mutation. Missing teeth are an intriguing disease marker because they are normally spotted early and conclusively. Knowing up front that you are at a higher risk for certain types of cancer could help guide screening guidelines for years.

So what’s the deal with the ovarian cancer link? Well, it’s been noted for a while that women are more likely to have hypodontia then men. Since hypodontia is likely caused by some sort of genetic mutation or disruption in development, it made a certain amount of sense to see if it was linked with cancer specific to women. The initial study linking missing teeth and ovarian cancer showed women with ovarian cancer were 8 times as likely to have missing teeth, but subsequent studies were less certain.  A 2016 meta-analysis showed that overall it appears about 20% of ovarian cancer patients have evidence of hypodontia, as opposed to the general population rate of 2-11%. Unfortunately there’s still not a definitive biological mechanism (ie a gene that clearly drives both), and there’s not enough data to say how predictive missing teeth are (ie what my risk as a healthy person with known hypodontia is). We also don’t know if more missing teeth means greater risk, or if it’s only certain teeth that prove the risk. So while we’re part way there, we’re missing a few steps in the proving causality chain.

Are there links to other cancers here too? Why yes! This paper from 2013 reviewed the literature and discovered that all craniofacial abnormalities (congenitally missing teeth, cleft palate, etc) seem to be associated with a higher family cancer risk.  That paper actually interviewed people about all their family members cancer histories, to cast a wider net for genetic mutations. Interestingly, the sex-linked cancers (prostate, breast, cervical and ovarian) were significantly associated with missing teeth, as was brain cancer. In some families it looks like there is a link to colorectal cancer, but this doesn’t appear to be broadly true.

So where does this leave us? While the evidence isn’t yet completely clear, it does appear that people who are missing teeth should be on a slightly higher alert for signs of ovarian or prostate cancer. Additionally, I’ve sent my dentist and my PCP the literature to review, since neither of them had ever heard of this link. Both found it noteworthy. It’s probably not worth losing sleep over, since we don’t know what the absolute increase is at this point. However, it’s good to keep in the back of your mind. Early detection saves lives.

5 Examples of Bimodal Distributions (None of Which Are Human Height)

Of all the strange things about statistics education in the US (and other countries for all I know) is the way we teach kids about the bimodal distribution. A bimodal distribution is a set of data that has two peaks (modes) that are at least as far apart as the sum of the standard deviations. It looks like this:

It’s an important distribution to know about, because if your data looks like this, your calculations for the average are going to be totally useless. For the distribution above for example, we’d get an average of (around) zero, which would tell us nearly nothing about the data itself, and would completely miss both peaks. So far so good. However, when this is taught in stats classes, the “real world” example most kids are given is human height….and human height is not bimodal. Bummer.

Given that it’s the start of the school year and all, I thought it would be a good time to provide teachers with some new examples. Now, depending on the underlying data set you might use, some of these examples may not make the “peaks separated by the length of the combined standard deviations” cutoff either…..but at least you’ll be wrong in new ways. That’s got to count for something, right?

  1. Starting salaries for lawyers On average new lawyers do well. In reality there are big winners and losers in the whole “getting a good job after graduation” game, and it shows in the salary distributions. Read the Above The Law complaint here.
  2. Book prices Book prices cluster around different price points, depending on whether your looking at paperbacks or hardcovers as God Plays Dice explains. If the gap between paperback and hardcovers isn’t wide enough for you, imagine you could pull price data for every book available on Amazon.com. You’d end up with a two modes, one for regular books and one for textbooks.
  3. Peak restaurant hours If you plotted a histogram of when every customer entered a restaurant on a given day, you’d end up with a bimodal distribution around 2 points: lunch and dinner. This type of histogram also tends to appear when you map road usage (morning and afternoon rush hours) and residential water/electricity usage (before and after work).
  4. Speed limits This one I actually couldn’t find much data on, but I’m guessing if you mapped out all the speed limits on every mile of road in the US (or maybe just your state), your distribution would end up clustered around 30/35 and then again around 60/65. Basically highways or regular roads. This distribution would also have the additional wrinkle of skewing differently based on whether we used miles of road or number of roads, but that’s a different matter entirely.
  5. Disease patterns There’s a rather fascinating two part blog post  by Jules J Berman that discusses bimodal cancer patters here and here. Basically these are cancers that appear similar but tend to hit rather different ages groups. For example Karposi’s sarcoma hits young men with AIDS and older men who do not have AIDS, and Berman argues that seeing these patterns should give us important clues about the diseases themselves. Possible explanations from Berman’s post:  1. Multiple environmental causes targeting different ages 2. Multiple genetic causes with different latencies 3. Multiple diseases classified under one name 4. Faulty or insufficient data 5. Combinations of 1,2,3 and 4.

Bimodal distributions are also a great reason why the number one rule of data analysis is to ALWAYS take a quick look at a graph of your data before you do anything. As you can see from the above examples, the peaks almost always contain their own important sets of information, and must be understood both separately and together in order be understood at all.

So what’s your favorite non-human height example?