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!

Measuring Weather Events: A Few Options

Like most people in the US this past week, I was glued to the news watching the terrible devastation Hurricane Harvey was inflicting on Houston. I have quite a few close friends and close work collaborators in the area, who thankfully all are okay and appear to have suffered minimal property damage themselves. Of course all are shaken, and they have been recommending good charities in the area to donate to.  As someone’s whose lived through a home flood/property destruction/disaster area/FEMA process at least once in my adult life, I know how helpless you feel watching your home get swallowed by water and how unbelievably long the recovery can be. Of course when I went through it I had a huge advantage….the damage was limited to a small number of people and we had easy access to lots of undamaged places. I can’t imagine going through this when the whole city around you has suffered the same thing, or how you begin to triage a situation like this.

However, as the total cost of Hurricane Harvey continues to be fully assessed, I want to make a comment on some numbers I’m seeing tossed around. It’s important to remember in the aftermath of weather events of any kind that there are actually a few different ways of measuring how “big” it is:

  1. Disaster Measurement:
    1. Lives lost
    2. Total financial cost
    3. Long term impacts to other infrastructure (i.e. ability to rebuild, gas pipelines,etc)
  2. Storm measurement
    1. Size of weather event (i.e. magnitude of hurricane/volume of rainfall)
    2. Secondary outcomes of weather event (i.e. flooding)

538 has a good breakdown of where Harvey ranks (so far) on all of these scales here.

Now none of this matters too much to those living through the storm’s aftermath, but in terms of overall patterns they can be important distinctions to keep in mind. For example, many people have been wondering how Harvey compares to Katrina. Because of the large loss of life with Katrina (almost 2,000 deaths) and the high cost ($160 billion) it’s clear that Katrina is the benchmark for disastrous storms. However, in terms of wind speed and power of the storm, Katrina was actually pretty similar to Hurricane Andrew in 1992 which resulted in 61 deaths and had a quarter of the cost. So why did Katrina have such a massive death toll? Well, as 538 explains:

Katrina’s devastation was a result of the failure of government flood protection systems, violent storm surges, a chaotic evacuation plan and an ill-prepared city government.

So the most disastrous storms are not always the biggest, and the biggest storms are not always the most disastrous. This is important because the number of weather events causing massive damage (as in > $1 billion) is going up:

Source of graph.
Source of disaster data.

However, the number of hurricanes hitting the US has not gone up in that time (more aggregate data ending in 2004 here, storm level data through 2016 here). This graph does not include 2017 or Hurricane Harvey.

Now all of these methods of measuring events are valid, depending on what you’re using them for. However, that doesn’t mean the measures are totally interchangeable. As with all data, the way you intend to use it matters. If you’re making a case about weather patterns and climate change, costly storm data doesn’t prove the weather itself is worsening. However if you’re trying to make a case for infrastructure spending, cost data may be exactly what you need.

Stay safe everyone, and if you can spare a bit of cash, our friends in Houston can use it.

The Signal and the Noise: Chapter 4

I’ve been going through the book The Signal and the Noise, and pulling out some of the anecdotes in to contingency matrices. Chapter 4 covers weather forecasts.

Chapter 4 of this book was pretty interesting, as it covered weather predictions from various sources. It presented some data that showed how accurate weather predictions from various sources were. Essentially the graphs plotted the prediction (i.e. “20% chance of rain”) against the frequency of rain actually occurring after the prediction.  They found that the National Weather Service is the most accurate, then the Weather Channel, then local TV stations.

While that was interesting in and of itself, what really intrigued me was the discussion of whether an accurate forecast was actually a good forecast. People watching the local news for their weather are almost invariably going to make decisions based on that forecast, so meteorologists actually have a lot of incentives to exaggerate bad weather a bit. After all, people are much less likely to be annoyed by the time they brought an umbrella and didn’t need it than the time they got soaked by a storm they didn’t expect. The National Weather Service on the other hand is taxpayer funded to be as accurate as possible, and may end up seeing their track record put in front of Congress at some point. Different incentives mean different choices.

SignalNoiseCh4

To give you an idea of the comparison, when the National Weather Service says the chance of rain is 100%, it’s about 98%. When the Weather Channel says it, it’s about 92%. When a local station says it, it’s about 68%. When Aaron Justus says it….well, this happens:

What’s a Normal Winter Anyway? (Boston Edition)

Mid-March is here, and all of Boston is breathing a sigh of relief that this winter was more “normal” than last winter. Last winter was completely record breaking in terms of snow, and we all have a bit of a hangover from it. I was discussing this with a few people at work, and we started to wonder what “normal” really looks like for this area. Obviously this meant I needed a graph!  I wanted to check out what the snow curve normally looks like for each winter, and I found some decent looking data here.  A few notes:

  1. The data is almost 100 years worth….1920 through 2016
  2. After 1936, measurements are from Boston Logan Airport. Apparently that’s when the weather station opened there. I’m not completely sure where they came from prior to that, but presumably it was somewhere in the area.
  3. For all data, the year means “season ending in”. So my 2016 totals include November and December of 2015.
  4. I only looked at November-April.  October and May have both had snow, but the snow that fell in those months has never gone over 1.5 inches for any season.

Okay, so what’s normal?  First I took a look by month. The blue box represents the middle two quartiles, or where half of all years fall. The lines on either end are the top/bottom 25% of years:

Snowbymonth

So it appears January and February are approximately equal for most years, but February can pack a bigger punch.

But let’s just look at averages for the months, then see where last year and this year fall:

Recentyearsvsaverages

Interesting. This shows that this year we actually had a slightly above average February, we just didn’t notice because last year was insane.

Okay, but what about total snowfall? Where are we so far?

Well, since 1920, here’s what it takes to make each quartile:

Min 8 inches
25% of winters < 28 inches
Median < 39 inches
75% of winters < 53 inches
Maximum 112 inches

As it stands right now, Boston has gotten about 25 inches of snow so far this winter. That puts us in the lowest quartile for snowfall. We’re not quite the least snowy winter in recent memory (2012, 2007 and 2002 all had less snow), but we’re certainly on the lower end. Only 18 years (since 1920)

So basically we have a year with legitimately low snow totals that was preceeded by a year with outrageous snow totals.Kind of explains the whiplash.

But where are we on the whiplash scale? Is this the biggest year to year change in snow totals ever?

Well, we hit a record for that this year for sure. An 87 inch difference in snowfall totals for consecutive years is pretty record breaking.  Interestingly though, there were two streaks I found that actually gave people whiplash for 4 years in a row. The  1994-1997 run, where the snow totals swung up to almost 100 inches for two winters (1994 and 1996) and then hit low totals on the alternating years (16 inches and 30 inches in 1995 and 1997, respectively).  2002-2006 was similar, though less dramatic.  In order to compete, 2017 will have to hit 90 inches or more of snow.

Don’t do that 2017, don’t do that.