Cookie Dough: Cold Killer?

Greetings, bowl-lickers.

A friend of mine who enjoys the odd clandestine spoonful of uncooked cookie dough suggested to me last night that I look into the risks involved in his filthy habit. (Just kidding - I regularly eat raw cookie dough by the scoop.)

We're told never to eat cookie dough because raw eggs may contain the bacterium Salmonella enterica, which can make you sick. Despite all the warnings, cookie dough eating is rampant in North America. Does cookie dough cause widespread poisoning deaths, or is it just another paper tiger? Read on to find out.



Salmonellosis: Symptoms and Rates

Any medical condition with a Latin name sounds scary. However, the majority of Salmonella infections cause gastro-intestinal upset and a fever for 4 to 7 days and then go away without formal medical intervention. If you're old, an infant, or have a weak immune system, you could need antibiotics to make your infection go away, and a particularly bad Salmonella infection can cause lasting conditions like arthritis or death. However, these big-ticket fears are relatively uncommon; this CDC study says the ratio of illnesses to hospitalizations to deaths for nontyphoidal salmonellosis is roughly 2,426 to 28 to 1.

The same CDC study estimates that the number of cases of salmonellosis in the United States is about 182 000 per year, or about 1 in 1 500; but since most infections go unreported it's really hard to tell. Its best guess is that salmonellosis from shell eggs causes about 2000 hospitalizations and 70 deaths per year: in other words, salmonella from eggs is about 1000 times less deadly than the flu (from this .pdf, page 2; this comparison is apt since both flu and salmonellosis are grave threats mostly to people with compromised immune systems).

Is Cookie Dough a Big Culprit?

Most of the salmonellosis outbreaks that make the news come from large-scale slip-ups where dozens of people get ill, rather than from small families tasting the occasional batch of cookie dough. Is this just because it takes a certain number of cases before a story is newsworthy, or is there another cause at work?

This CDC page warns that in large-batch recipes where 500 eggs are used the Salmonella risk is greater, since one contaminated egg could taint the whole batch. So what's the risk of getting salmonellosis from eating cookie dough from a two-egg recipe?

This study estimates that only 1 in 30 000 eggs is potentially contaminated with Salmonella, so at most there is a 1 in 15 000 chance that your dough is going to have any Salmonella bacteria. (If the first egg doesn't have Salmonella, the second egg has a smaller than 1 in 30 000 chance of having it too, so 1 in 15 000 is an over-estimate of the risk.) Assuming that it's certain that you will catch an infection from tainted dough, that puts your risk of death from tasting the dough at less than 1 in 36 million; if you have a healthy immune system your risk is considerably smaller. The daily chance of getting a flu as bad as a non-fatal flu-like Salmonella infection are 1 in a few hundred, so you really don't need to worry about salmonella from cookie dough: background risk levels are much higher.

EDD, LED and GHAF

Let's put that 1 in 36 million figure in perspective. The Equivalent Driving Distance (EDD) is just under 2 miles (for those new here, that means a 2-mile car trip is as likely to kill you on average as eating 2 raw eggs) and the Life Expectancy Decrease (LED) is less than 37 seconds (eating 2 raw eggs decreases your life expectancy by only 37 seconds - here I assumed on average my readers might have 42 years left in life and divided by 36 million). For more on the LED and EDD risk metrics, see this introductory blog post and this wiki page for recording risk levels.

So on average the risk of being killed by your baking is negligible. But is the fear over-hyped? Considering there are 294 000 Google hits for "salmonella raw eggs America" and only 70 Americans die of Salmonella from raw eggs, the Google Hits per Annual Fatality (GHAF) hype-metric is 4 200: about as high as for West Nile virus. (See an introduction to the GHAF metric here and a list of GHAFs for various risks here.)

Conclusion: Lick On!

Eating cookie dough gives you a negligible risk unless you have a particularly weak immune system. Whip yourself up a batch and eat it all: it really doesn't matter. Oh, and please save me a spoonful while you're at it.

Bon Appetit!

LeDopore

Metrics Contest

Greetings, high-rollers!

In today's post, I'm announcing a new, exciting contest for my Many-Ideas readers: the Metrics Contest!

LED, EDD and GHAF Recap

If you're new here, let me fill you in a bit on the history and aims of this blog. I'm interested in putting risks into perspective and in deflating over hyped issues.

But how do we know how risky a certain activity is in a way that's easily understood? And how do you quantify hype?

To give risk probabilities a human touch, I've introduced two new metrics: the life expectancy decrease (LED), which gives you the expected amount of time your lifespan decreases from engaging in the said risky behavior, and the equivalent driving distance (EDD), which finds the distance you would have to drive to accrue a risk comparable to the activity in question. The LED is calculated by multiplying 85 years by the chance the measured risk will kill or seriously maim you, while the EDD is calculated by multiplying the risk by 1 billion (10^9) miles and dividing by 14.6, since in 2005 in the US there were 14.6 fatalities per billion miles driven.

The GHAF measures undue hype, not just pure risk. Bigger risks deserve more attention, but they don't always get it. "GHAF" stands for "Google Hits per Annual Fatality," and measures the ratio of the attention an issue gets to the real threat it poses. It's a very approximate measure, but the GHAF for different risks is so variable (from about 1 to over 100 000) that it's still useful in identifying over hyped issues.

The inaugural LED and EDD post is here, and the post introducing the GHAF is here. If you're new here, check them out to get an idea of how they can be calculated.

Risk and Hype Lists

There's a wiki to keep track of both the risks of certain activities (here) and the GHAF of certain phenomena (here). There are many fascinating cases of hype which these lists miss, which is why I'm holding this contest.

Contest Rules

Calculate the EDD, LED and GHAF for a risk of your choosing, and post it to me along with an idea for a Many Ideas blog post. On April 23rd I'll announce my favorite, and I will do a full investigation and posting on the winner's topic.

Submissions will be rated for originality (5 pts) accuracy (5 pts) and for how much they reveal about out risk biases (15 pts). Entering your EDD, LED and GHAF to the wiki pages earns an extra 2 points.

May the juiciest entry win!

The Google Hype-Meter

Greetings, West Nile mosquito-swatters.

I hope that by now you will be familiar with my style of putting over-hyped risks in their place. But how do you determine how over-hyped a problem is? Today I'm going to introduce a new metric to assess how out-of-proportion a particular death threat is: the Google Hits per Annual Fatality or GHAF metric.

Google and Hype

First of all, let me admit that reducing such a nebulous idea as "hype" to a number is an inexact science at best. However, I happen to be an inexact scientist: the perfect blogger for the job.

The people who post web content are not representative of the human race as a whole, so if there's something which netizens preferentially talk about, Google is going to reflect that bias. However, in most cases this bias will distort reality by at most about a factor of 10, so any enormous differences in the whole-world hype devoted to certain risk factors should be also present in a subject's Internet chatter. Luckily, some small risks are so enormously exaggerated that even an inexact measure like the GHAF can find them with confidence.

Calculating the GHAF

So, if we're agreed that Google hits will approximate the amount of talk on a subject, we can divide the number of hits by the annual death rate of a scare to get the GHAF, a relative measure of how much that particular problem has been overblown. Let's take a look at a few real-world examples of the GHAF.

Raw Data

• Malaria in Africa (GHAF = 1.5, 3 million Google hits [1] per approximately 2 million annual deaths [2])
  
• Cancer in the United States (GHAF = 94, 54 million hits [3] per 570 280 annual deaths: page 1 of [4], .pdf warning: 6 MB)
• West Nile Virus in the United States (GHAF = 5 500, 911 000 hits [5] per 165 annual deaths [6])
• vCJD, the human disease from eating a mad cow, worldwide (GHAF = 81 000, 1.4 million hits [7] for 139 cases over 8 years [8] - see my blog entry for an editorial[9])
  
• Alligator Attacks in the United States (GHAF = 293 000, 461 000 hits [10] per 1.57 annual deaths [11] - possibly the fatality rate is underestimated by this list and possibly a lot of the Google hits came from attacks on non-human targets)
  

Summary

The GHAF hype metric has a huge variability. It is a few thousand time greater for West Nile in the US than for malaria in Africa. Working from the assumption that most human life should be treated with roughly the same degree of care, these wildly differing GHAFs indicate that we spend far too much time worrying about the wrong things. With the GHAF, we can measure just how skewed our fears are.

The above list is far from exhaustive; does anybody want to look into adding traffic deaths or killer bees? I've set up a wiki page to keep track of the GHAFs of various risks. Feel free to add to it!

In any case, there's a huge variation in how much hype a risk gets compared with the actual danger involved. I realize there are only so many articles one can read about a certain risk before becoming inured to it, so one would expect the GHAF to be lower for real risks as not as much press will go to the millionth victim as to the first. However, the number of Google hits a risk gets is not even an increasing function of associated body count, showing that our problems run deeper that just weariness over old news.

Conclusions

I've already introduced two new measures of danger, the life expectancy decrease (LED) and the equivalent driving distance (EDD). However, these measures only ask how dangerous an activity is; they do not report how much that danger has been magnified by the media. With the GHAF, we can quantify just how out-of-proportion the hype is around a certain fear, and perhaps allow this measure of exaggeration to shape policy.

I look forward to your additions to my wiki page. What will my intelligent readers discover?