Someone Was Wrong On The Internet

To content | To menu | To search

Wednesday 28 January 2015

Vaccination for Idiots

This what happens when you vaccinate in a society:


If you want to know what happens when enough individuals in a society opt out of vaccination, I have some articles about the current measles outbreak I could send you...

For the people who got measles at Disneyland, out of the California victims for whom vaccination data could be verified, 82% were not vaccinated.

refs (accessed 28 Jan 15):

  • (it's their graphic which I redrew to improve clarity)

Monday 27 May 2013

There's a 0.0003% chance that the TSA will give you cancer!

If this wasn't so sad, it would be funny.

A piece from the New York Times Opinionator blog on Saturday was a rather self-indulgent rant about x-ray machines used by the TSA in airports. You can find it at:

I found it mildly amusing but the cancer rant got old fast. The consistent usage of "cancer machine" and "radiation chamber" was a bit much, at least for me.

I can grant the author his conceit in avoiding the TSA's look-at-you-nude booths in airports. He is entitled to do so. Most people find the x-ray and milliwave booths obnoxious, including me, and they are probably overkill as a deterrent against high-jacking and terrorism - though that last bit is just my own personal opinion. What I take exception to is the bad science: that x-ray imaging booths will cause gobs of cancer regardless of the epidemiology of cancer risks and regardless of what scientific experts - the kind you can actually cite - say on the subject.

I guess the author thinks that scientific concensus is a pile of crap when compared to his fear of radiation.

He degrades information from experts of radiation and cancer risks - and then turns around at the end of his rant to bemoan the lack of scientific studies on TSA x-ray booths, which he seems to believe will verify his fears. I find this rather odd given that I have had no problem finding peer-reviewed studies on the safety of airport back-scatter x-ray detectors by independent researchers; for example: Mehta, P, and Smith-Bindman, R. (2011), Airport Full-Body Screening - What Is the Risk, Archives Intern Med., 171(12):1112-1115. Now in the academic world of peer reviewed journal, insufficient citation and shallow research to find sources is the dreaded sin of "doesn't know the literature." Basically, this means the author didn't do his homework in investigating his subject; however, journalists are not required to meet the rigorous standards of fact-verification that hard scientists have to endure, and because not enough people challenge what journalists report, they can get away with it. The author of this blog post did actually cite someone though. He cited one other New York Times blog as the sole support for his statements. You can find the cited blog here:

This seems to be a pattern in the blogosphere: one non-expert blog citing another non-expert blog, and it has all the problems that arise when you rely too much on secondary and tertiary sources. I found that the cited blog post was far less annoying than the first but it still made some rather dubious statements. Here's one I liked:

For security reasons, much about how the (x-ray) machines work has been kept secret.

Huh? What? Are you kidding me??? What's secret about back-scattered x-rays? Back-scattered x-rays, also known as Compton scattering, have been around since Compton discovered them in 1922 in St. Louis. He got the Nobel Prize in Physics for it. (ref:, accessed May 27, 2013).

The technology of back-scattered x-rays is a no-brainer. You pick an emitter and x-ray target combination to get the exact x-ray wavelength you need for the imaging, you design the detector, build a detector-to-computer interface and then write up some software. I could probably design and build one myself, though I would probably have to hire an electrical engineer to get the detector-to-computer interface right and a good machinist to make the mechanical parts.

The authors of these two blog articles both make vague claims regarding secrecy and lack of information on x-ray scanners. I think they might have gotten confused over the potential deployment of mass-transit and van-portable scanners. This concerns a non-airport use of scanners that may be rather invasive toward people just walking around on the street who have no means to decline, protest or even consent to being scanned. The flap might be a real one and a political activist group has filed a freedom-of-information act request to liberate about a thousand pages of paperwork on non-airport use of scanners (see But that's a whole different issue than airport scanners. It has nothing to do with the air-port scanner other than the use of the Compton effect.

There have been some objections to the x-ray booths at airports from some real science and medicine types, like the 2010 publicly-issued letter by four University of California San Francisco (UCSF) researchers. This non-peer reviewed protest over airport x-ray scanners got a lot of press. If these guys had gone through peer-review, they would have spared themselves some embarrassment. You can read the letter for yourself at

The concerns in that letter about safeguards for when an x-ray scanner booth breaks down or malfunctions are legit. The TSA has not been forthright with details on engineering controls and emergency response when breakage happens. But those are engineering concerns and therefore fixable if indeed they are real problems. The letter's science regarding penetration and dose, however, blew it. It looks like four bright profs from UCSF got the basics of received dose vs. penetration wrong. It's an understandable mistake for four medicine-oriented researchers to make. Medical x-rays and material scanning x-rays are different in mechanism and in wavelength. Medical x-rays are directly transmitted and penetrate deeply and through the body. Back-scatter x-rays, also know as Comptom scattering, do not penetrate deeply, but rather they reflect off clothing and skin. The physics is quite different for Compton scattering than for transmitted medical x-rays. The capture of the reflected x-rays are used to make the types of images used in the airport scanners. The radiation dose objections were neatly retorted by the inventor of some of the first back-scatter body scanners during congressional hearings in late 2010. Although it is a bit dense, you can read the rebuttal to the four UCSF profs here:

The best bit in the cited blog article, at least for me, is as follows:

But some experts are less sanguine, and questions persist about the safety of using X-ray machines on such a large scale. A recent study reported that radiation from the machines can reach organs through the skin.

The author of the cited blog is referring to a Marquette University study (M. E. Hoppe, T. G. Schmidt (2012), Estimation of organ and effective dose due to Compton backscatter security scans, Medical Physics, 39 (6), 3396-3403). The researchers built a computer model and then used it, getting results that a very small amount of x-ray radiation may actually gets through the skin to tissues immediately below the skin. The researchers who published the study did not themselves think this was a big deal since the intensity of the x-rays used in airport scanners is really low, hugely lower than a medical x-ray. Please excuse me for this, but big fat hairy deal! I just love sensationalism in reporting. Don't you? Hey, is that sarcasm I'm smelling?

The cited blog went on further to say:

In another report, researchers estimated that one billion X-ray backscatter scans per year would lead to perhaps 100 radiation-induced cancers in the future.

I confess, it was this statement that convinced me that this was worthy of my blog.

Okay, let's talk about risk. Risk is based on statistics. For real illnesses and injuries, the stats that exist are hard numbers based in the USA on reports sent to the Center for Disease Control (CDC) by doctors. For radiation-based illnesses, the stats that you read about in the newspaper are based on computer models that extrapolate from the very small number of illnesses known to have been caused by ionizing radiation - that includes x-ray caused cancers. The datasets on radiation-caused cancers are not huge. Most of the data is from Hiroshima, Nagasaki, and Chernobyl plus a very small number of nuclear fuel processing exposures and accidental medical irradiations. Because of the sparse data, most of the risk assessments for things like cancers caused by x-rays are based on extrapolations of the sparse data. The problem with extrapolation is that it's based on assumptions of how the data would behave in the gaps where real numbers are missing. If we had all the data we wanted, could we fit a straight line through it or would it look like a curve instead? Those are the sorts of assumptions that go into extrapolations and computer models. And sometimes, the intelligent guesses that modelers make are incorrect. Statistics are only as good as the math behind them.

So back to the study reporting 100 cancers per one billion scans. The study is Brenner, D. J. (2011), Are X-Ray Backscatter Scanners Safe for Airport Passenger Screening? For Most Individuals, Probably Yes, but a Billion Scans per Year Raises Long-Term Public Health Concerns, Radiology, 259, 6-10. That has to be one of the worst journal article titles that I have ever seen but it's a good piece of work that you can read here: (accessed May 27, 2013).

If we go back and look at the cited blog, the author phrased things to imply that the results of the two studies were big and alarming concerns. Let's look at the excerpt as a whole now:

But some experts are less sanguine, and questions persist about the safety of using X-ray machines on such a large scale. A recent study reported that radiation from the machines can reach organs through the skin. In another report, researchers estimated that one billion X-ray backscatter scans per year would lead to perhaps 100 radiation-induced cancers in the future. The European Union has banned body scanners that use radiation; it is against the law in several European countries to X-ray people without a medical reason.

In context, it should be obvious that the author's goal in this paragraph is to imply there is reason to be alarmed, that this cascade of items is evidence of the danger of airport x-ray scanners. Now I'm going to refrain in saying anything about the European Union. I mean, seriously, these are the folks who tried to regulate the shape of vegetables and ban olive oil served in cruets! EU legislators make the current House of Representatives look sane and reasonable! What those people want to do in Europe is not germane to x-ray scanners in airports in the USA. We have already discussed the penetration issue. So what's left is the statistic that the author wants us to believe is a bad thing.

I'm am not going to say anything like 100 extra cancers a year is an okay thing. That's not the issue. The issue here is the perception of risk. Those 100 cancers are not real: they are a statistic generated by extrapolation in an epidemiology model. Cancer will happen to people whether we want it to or not. Numbers like 100 cancers for every 1 billion scans is a risk assessment. It is a reflection of what we think reality will be, but it's not reality itself. There's another thing to consider here, especially when reading stuff like this in the news, and that's the word cancer itself. Cancer is a panic word. It used to be that most folks who got cancer died and the death was unpleasant and painful. Cancer is still a lousy way to die but it is no longer a death sentence. Many cancers now are treatable, survivable and in some cases, preventable. But the very word is still a cause of fright, much in the way that leprosy or polio used to be. The cancer disease group is not the problem here; it's the perception of cancer in American society that's the issue. A journalist need only drop the word into a news report,like the two New York Times blogs here and be assured that the usage will help garner readers. The word cancer in the news is like a giant electrical billboard flashing the fear of the armageddon in a nightmare future that could be our fate. Cancer is a good word to drive readership and readership drives ad revenue.

One hundred cancers per one billion scans: that's an incidence rate, not a death rate. There was no estimate from this study of how many of those cancers would result in death. Now once scanners are installed in every airport, the TSA projects that they will be doing about one billion scans a year, and that translates to 100 cancers per year. That's a projection and it assumes that every scanner uses back-scattered x-rays. We need to translate this into the standard form for disease rate that the CDC uses, cases or deaths per one hundred thousand. The population of the USA is a little over 300 million. Dividing 100 cancers by the population of the country gives us a cancer incidence rate of 0.03 cancer cases per 100,000. Here's another way to say this: in any given year, you have a 0.0003% chance of getting cancer from an airport back-scatter x-ray scanner. You should probably feel really scared now.

Okay, I admit it. I am rather underwhelmed here.

Why is this a trivial number? Easy. Look at real cancer incidence rates for a comparison. It's easy to do so. Check out the National Cancer Institute Fast Stats website at: (accessed on 5-27-2013). It's a cancer statistics tool. I ran the fast stat tool for all incidences of cancer for both genders and all ages. Here's a look at the data for the last several years:


Yep, you read that graph correctly! The current national cancer incidence rate is about 450 per 100,000. That's a number that's based on real numbers of actual reported cancers - it's not from a computer model. So I hope you can forgive my being underwhelmed but the alarming and scary thought of 100 cancers per year from airport x-ray scanners. For comparison, here's some data from the CDC Morbidity and Mortality Datasets. Compared to 100 cancers, more children are murdered every year, between three to four hundred. Between 1000 and 2000 people will be struck by lightning, half of whom will survive. Between 35000 and 40000 will commit suicide. Between 0 to 12 people will be bitten by poisonous snakes, most of whom will live. An estimated 45000 people die every year because they couldn't afford health insurance. One to two people per year in the USA die of rabies every year. Between 30000 and 40000 will die in car accidents; about 10000 of those deaths are due to drunk driving.

People opt out of going through the airport x-ray scanner because of fear of radiation. They should really be scared of travelling in cars.

Monday 6 May 2013

A Perfect Storm of Lyme Disease!

Today's culprit is an article from last year in the Huffington Post last year. Its title is:

Lyme Disease: The Perfect Storm is Heading Our Way

The author is Leo Galland, M.D. You can read it for yourself at

The reason I'm discussing this article is because it made a prediction that did not come true. The author and other scientists predicted an increase in the number of lyme disease cases based on two factors:

  1. 1.) a warm 2011/2012 Winter, and
  2. 2.) a large acorn crop in 2010 followed by a smaller-than-usual acorn crop in 2011.

Ticks live for two years, assuming they survive their larval feeding and their one and only Winter attached to a host. Galland reasoned that more ticks would survive in

an unusually warm winter, which left deer ticks alive, hungry and looking for a meal.

His statement about acorns was the following:

The mice feed on acorns and store them for winter. The fall of 2010 brought a bumper crop of acorns, which led to a surge in the mouse population and created abundant homes for tick larvae last spring (2011). In the fall of 2011 the acorn crop was the smallest it's been in two decades, decimating the mouse population over the winter and leaving a huge number of displaced nymphs that are looking for warm-blooded hosts, like humans.

To be frank, Galland presented his prediction as sensational news and I confess that it annoyed me. He began his article with:

Blood-sucking ticks coming to a field and forest near you. That may sound like the latest horror film, but unfortunately it is a reality due to a surge in ticks that spread Lyme disease this spring.

To make matters worse, he went on to brag:

media interest in Lyme disease appears to be growing with the threat. At the start of the month I was interviewed on Martha Stewart Living Radio about Lyme disease.

Galland did include a long list of references, three of which looked at the ecology of Lyme disease vectors. One of these, Schauber et al. (2005), stated in its abstract:

Acorn production and mouse abundance....were the strongest predictors (r =0.78)....(of) Lyme disease incidence

The above makes a case for correlations between acorns, mice and ticks and a regression coefficient of 0.78 is not bad. What's missing is the formal quantification of the two year lag for the acorn crop in this paper or in the other two ecology references. Where then did Galland get the details on the acorn-mice-tick-Lyme disease chain? It's possible that he used Ostfeld et al. (2006), which is a highly cited article. He might have gotten it out of Ostfeld's book on the ecology of Lyme disease, a book I don't have access to and can't afford to buy. He might even have gotten it from a blog interview of Ostfeld last year on a website called Overall, Ostfeld is the go-to source for info on the acorn-mice-tick-Lyme disease connections. Let's look at Ostfeld et al. (2006).

Ostfeld et al. (2006) is online at PubMed (, accessed May 6, 2013), Ostfeld et al. (2006) looked at multiple weather effects as well as deer populations, chipmunk populations, mice populations, larval tick populations, nymph tick populations, infection density of tick nymphs, and acorn abundance. Ostfeld et al. (2006) did mention some interesting correlations in their paper from previous studies.. One interesting one was:

In the laboratory, ticks experience high mortality when exposed to low humidity and high temperatures. Consequently, hot and/or dry springs and summers have been postulated to reduce subsequent nymphal tick densities and Lyme-disease risk.

Another correlation mentioned was:

Because adult I. scapularis (deer ticks) feed predominantly on white-tailed deer, much research has evaluated the impact of variation in abundance of deer on abundance of ticks. When deer are eliminated from some habitats by hunting or fencing, the abundance of ticks typically is strongly reduced. Studies comparing natural variation in deer abundance with that in tick abundance are less conclusive; some have shown strong associations, whereas others have not.

The Ostfeld et al. (2006) study was not a single variable, laboratory-only effort. The conclusions of Ostfeld et al. (2006) were based on the statistical analysis and multivarient modeling of 13 years with of data collection in oak-containing forests in upstate New York. Their work was very data-driven and they used their 13 years of data to tightly constrain their models. When all was said and done, they noted that:

Among all univariate models, NIP (nymph infection prevalence, i.e. the numbers of Lyme carrying-juvenile ticks) responded only to the density of acorns in year t−2, and this relationship explained only 16% of the variance in NIP. Models incorporating the climate variables, deer variables, DOL (prior year's density of tick larva), mice, chipmunks, and total rodents performed no better than the means model. Because none of the independent variables other than acorns t −2 produced an improvement over the means model, there was no justification for testing multiple regression models.

In plain English, Ostfeld et al. (2006) found that Lyme disease risk from ticks correlated positively with an abundant acorn crop two years prior, but the correlation wasn't all that strong. They also noted that no other variables in their models caused statistically significant increases or decreases in Lyme disease risk. This was a surprise as far as deer were concerned because for years, people tried to correlate Lyme disease with deer ticks and deer abundance.

Their concluding paragraph in the discussion of their results stated:

Climate, deer, and acorns each have been proposed as primary determinants of temporal variation in risk of human exposure to Lyme disease, as measured by abundance and Borrelia-infection prevalence in nymphal Ixodes ticks. Using a model comparison approach and a 13-year dataset, we found weak support for climate variables, no support for deer, and strong support for an effect of acorns, mediated by acorn effects on white-footed mice and eastern chipmunks, which host many larval ticks and are competent reservoirs for B. burgdorferi.

It is a safe assumption that Galland had one of Ostfeld's papers or books on hand when he wrote about the acorn to mice to tick connections.

Blog note: Borrelia burgdorferi is ...

The other ecological study cited by Galland was Keesing et al. (2009) (, accessed May 6, 2013). In this study, the researchers caught white-footed mice, eastern chipmunks, grey squirrels, opossums, veeries and catbirds. They then penned their animals and deliberately introduced larval ticks to the captive population. When the ticks fell off after 3 days (the average duration of feeding), the researchers would remove one species and reintroduce more larval ticks to the remaining animals. This experiment was designed to see how that species removal would affect the redistribution and survival of ticks among the other five. It was a large and complicated experiment and I admit here than I glossed over some of the details for the sake of brevity.

Here are some Keesing et al. (2009) results: the common white-footed field mouse was a tick host par excellence. Fifty percent of the ticks that tried to attach on mice stayed on mice until they were engorged and fell off. Other animals were more aggressive with grooming, biting or rubbing ticks off. Opossums and squirrels were especially good at removing ticks, managing to get rid of 96% to 83% of all ticks, respectively, that tried to attach and feed. Most of ticks that were groomed off were consumed by their hosts. For all the other animals in the study, between 70% and 80% of the ticks failed to attach.

Surviving ticks

This figure is from Keesing et al. (2009).

Keesing et al. took their data on tick attachment and survival and used it to model the risk of Lyme disease. The model results were somewhat surprising. Removing mice caused the density of infected nymphs (DIN) to fall. Removing opossums caused the DIN to increase. With the caveat that the experimental set-up was a closed system, the Keesing et al. results for mice appear to be the exact opposite of what Galland stated in his article.

Predictions, scientific or otherwise, can only be validated by data after the fact. Galland's prediction of a "perfect storm" of Lyme disease for 2012 should be compared to the actual numbers of reported cases. If Galland was right, there should be an observable increase of Lyme disease in 2012. We have this data but first we need to do something about the change in counting Lyme disease cases that occurred in 2008.

Prior to 2008, Lyme disease statistics were reported only for confirmed cases. Starting in 2008, both confirmed plus probable cases were reported. These numbers are available for the years 2008 thru 2011. Validated statistics for 2012 are not available but an unvalidated confirmed+probable number was reported in the CDC's Morbidity and Mortality Weekly report for the last week in 2012.

Using the data from 2008 thru 2011, we can calculate an average ratio of confirmed+probable cases to confirmed only cases. The average ratio is 1.28. We can use this ratio to estimate confirmed+probable cases prior to 2008 and also to estimate a confirmed only number for 2012. All these numbers are included in the table below.


One can see clearly from the table that Galland's and Ostfeld's predictions of Lyme disease disaster were wrong. Lyme disease occurrences actually dropped in 2012 with respect to the year before. So what went wrong with the prediction? Nobody knows! The problem here is that we're dealing with a truly complex system where some hitherto unidentified variable just overrode the acorn-mice-tick correlation. This tells us that there are still some unconstrained variables out there waiting to be discovered. The cause could be anything that's plausible. As just one example, there could have been a tick die-off because of the extremely dry and hot weather during the Summer of 2012, the third hottest Summer ever recorded. Or perhaps there really was a mouse die-off from inadequate forage during the Winter of 2011/2012, as predicted, leading ticks to search for new hosts. But as Keesing et al. (2009) suggested, those ticks could have attempted attachment with tick-killing animals like squirrels and opossums, leading to the death of those ticks and thus reducing tick numbers and Lyme disease risks. One can make many plausible hypotheses to explain the unexpected decline in Lyme disease cases in 2012, but without more data on possible new variables, it's nothing but speculation for now.

For an MD, Galland's no ecologist. His article never mentioned the west coast blacklegged tick, which is the Lyme disease carrier west of the Rockies. Galland also showed no awareness that some regions in the USA are free of Lyme disease or that Lyme disease hot spots include the northeast, Minnesota, and Wisconsin. These are sins of omission which become apparent when we look at and compare a blog interview of Ostfeld posted on the website (ibid.). It looks like Galland borrowed heavily from the Ostfeld's blog interview (ibid.), including some section titles and references verbatim. Ostfeld qualified his tick surge estimate as valid only for the northeast and only for the deer tick, something Gallard did not do. Missing these caveats was not great journalism on the part of Galland, in my not so humble opinion.


I have to wonder if Galland and Ostfeld are in cahoots with each other since the website is owned by Galland and Ostfeld posts there..

I have a personal feeling that Galland was after sensationalism deliberately. Here's why I think so:

  1. His title and opening paragraphs used sensational wording, as we noted earlier (sensational claims)
  2. He cherry picked research results to support his hyped-up statements (data massaging)
  3. His reference section was blatantly cut and paste (false impression, bad style)
  4. He made an alarmist statement regarding the alleged failure of Lyme disease treatments (sensational claims)
  5. He opined that chronic Lyme disease is uncurable (sensational claim)
  6. He posted his article to a news aggregater with a reputation for pseudo-science and mostly amateur efforts with no editorial review.

Some of the practices in the above list speak for themselves but other need some explaining. We need to look closely at Galland's statement that the standard Lyme disease treatment of antibiotics is a failure. His evidence consists of journal articles reporting on failed treatments. He lists thirteen such articles in his references section. He ignores studies that contradict his stance on treatment failure: this is another sin of omission, one that is significant. While he told no outright lies, Galland's presentation of his failure data and his lack of discussion on alternative hypothesis made it appear that Lyme disease was an unstoppable epidemic with poor chances of successful treatment.

To test Galland's assertions, I visited the Center for Disease Control (CDC) website looking for data. The CDC is the best place to obtain data on all sorts of things like the incidence of various diseases and the causes of death, to name two examples. It's a gold mine of robust data. The CDC reports that 10% to 20% of people with Lyme disease do not respond to treatment by antibiotics in the short term (, accessed May 6, 2013). Antibiotics administered soon after infection are usually successful, whereas antibiotics administered many weeks to months after infection have a greater risk of failing, accessed May 8, 2013). In a soundbite: antibiotics administered soon after infection have an 80% probability for successful recovery. That's not a failure. Antibiotics work for most people in recovering from the early stages of Lyme disease.

I opine that Galland cherry-picked those 13 journal articles because they supported his allegation of treatment failure. It's a form of massaging the data to fit your hypothesis and it's bad science.

As to whether chronic Lyme disease is uncurable, the CDC notes that most people with persistent symptoms eventually get better on their own regardless of whether antibiotics are administered; however, the recovery rate can be extremely slow for some people (, accessed May 7, 2013). The bottom line is that Lyme disease, like shingles and syphilis, is an infectious disease which should be treated as soon as possible after infection. Like late-stage syphilis, chronic Lyme disease may be difficult to impossible to treat. For most people, it looks like the only effective cure for chronic Lyme disease is time; but there are still a few sufferers left over who never improve.

Stuff like Galland's article does real damage. When a journalist or blogger writes up a scientific result as the latest and greatest breakthrough ever, and then caps it with a sensational title, readers become rather cynical about science reporting and about science in general. Every time the content of an article doesn't live up to its title, another chip of respect for science is lost. But it's not scientists who are doing this - it's journalists. Slapping an outrageous reader-bait title on a science-based article has become the new norm for internet reporting. It happens all the time. There are so many misleading and sensational headlines on the internet that they become meaningless rather quickly. It's like a real world varient of the boy who cried wolf. When someone makes a science-based prediction, like last year's tick prediction, and it fails, the worth and credibility of science is further eroded. The only thing sensationalism really achieves in the news media is the attraction of readers toward webpages with advertising or phishing scams. The content and the presentation of reporting has been bent to maximize viewer ratings and advertising exposure. There's really very little real news anymore; it's all thinly disguised entertainment and the facts be damned.

Why am I suddenly reminded of the razzle-dazzle song from Chicago?

Wednesday 20 March 2013

Black Lung Cases Go Up While Black Lung Deaths Go Down.

While pondering a return to deaths caused by nuclear energy, I decided to look at the number of nuclear industry deaths vs. the number of coal mining and coal-burning power plant deaths. Doing this right should involve not only direct deaths (i.e. death by industrial accident) but also indirect deaths from chronic occupational diseases. As I was collecting my data, I spotted a handful of news reports from last Summer claiming a resurgence of black lung disease. Two of those reports were done by NPR, a news outlet I like and usually trust for unbiased news.

NPR reported an "surge" of black lung cases (, accessed 3/21/13):

Incidence of the disease that steals the breath of coal miners doubled in the last decade, according to data analyzed by epidemiologist Scott Laney at the National Institute for Occupational Safety and Health (NIOSH).

The NPR report showed the following graphic on their website as support for their statement and cited the National Institute for Occupational Safety and Health ("NIOSH") data (ibid.):


The NPR report along with several other news reports (e.g.,, accessed 3/20/13) claim an increase in black lung cases, especially in coal miners with over 25 years experience and in miners with relatively short experience. The upward trend in the bins on the far right of their graphic appears to support this. (Since this data is based on a NIOSH program for screening miners to find black lung symptoms, I'm labeling this as "screened-miner data" in the rest of this post.)

There's a problem here. CDC numbers for black lung incidence have some big data gaps. Here's the raw NIOSH data fresh off of the CDC website (; accessed 3/20/13):


It is a bit disturbing that in categories with no data, there is a percentage reported. This is a problem since the missing data makes it impossible to test the claim that decadal black lung rates have doubled. There is a possible workaround and that is to use the number of annual black lung deaths. People with advanced-stage black lung do not live long so any increases in the number of new black lung cases should be reflected in the number of black lung deaths. Oddly, this isn't apparent in the death statistics. Here's NIOSH's own graphic for black lung cases by year (; accessed 3/20/13).:


There is a way to possibly reconcile the news report claims and the actual raw NIOSH data. The news reports look at the percentage of screened miners with black lung symptoms as revealed by chest x-rays, whereas the raw death statistics deal with death only. Because of this, it is possible that a real increase in black lung cases has not yet had time to impact the reported rates of black lung deaths. If this is the case, then there should be an increase in annual black lung deaths in the immediate future.

There is a second possibility to account for the uptick shown in the NPR graph for the most experienced miners. The NPR graph lumps all the screened-miner data into five year averages. Given the obvious wobble in the annual NIOSH death figures, the apparent increase in the screened-miner averaged data could be a statistical fluke. It's an old trick to massage one's numbers when binning by changing the bin size or shifting the bin position. The trends shown on the NPR graph of NIOSH data are not as nice nor as conclusive is one uses a smaller bin size. Any real trend of increasing black lung cases should be as apparent in the annual data (bin size = 1 year) and in the half-decade data (bin size = 5 years). Here's the NIOSH balck lung incidence data plotted by year:


Looking at the black lung rate data on an annual basis shows that there is a lot of variability from year to year. The one period that this is not true is the 1990s where the rates smoothed out. Given the overall variability, it is possible that the hypothesized increase from the 1990s to the 2000s is really the result of data variability. Given the low overall numbers of black lung cases, variability is not at all surprising. This is how a lot of small datasets behave. At this point, one can argue that the 1990s data are the odd man out here due to their lack of variability. Such a hypothesis is equally plausible compared to a claim that black lung cases have doubled. The variability in the annual plot of black lung rates calls the decadal increase in black lung incidence into question. Given the small number of data points and the gaps in the discrete data, the increased black lung incidence rates are a dataset with some troubles.

Another problem with trying to use the screened-miner data is that the screening may not be representative of all miners because NIOSH screening for black lung is voluntary. There is no real control on who gets screened. A further factor involves where NIOSH collected their data. NIOSH offered screening to miners in 16 states; however, NIOSH offered enhanced additional screening to underground miners in just the "hot spot" states of Virginia, West Virginia and Kentucky (, accessed 3/21/13). This raises the possibility of real bias in the NIOSH screened-miner data both by area and by mine type (underground vs. surface).

Regardless of the decreasing death rate, researchers at NIOSH do believe that the number of black lung cases is increasing (e.g., CDC, Pneumoconiosis and advanced occupational lung disease among surface coal miners--16 states, 2010-2011: MMWR Morb Mortal Wkly Rep. 2012 Jun 15;61(23):431-4); however, even if the black lung rate doubled from the 1990s to 2000s as reported by NPR, that rate would still be an order of magnitude less than rates for 1970s. And this is what NPR labeled as a "surge" in black lung cases. It is worth noting that the news reports appear to have targeted and emphasized the increased number of black lung cases in the youngest and oldest miners when compared to the non-sensational presentation of data on the NIOSH website and in peer-reviewed studies by NIOSH researchers. A quick cruise through recent papers and abstracts on tells a different story from the news reports. After looking at long-term rates of black lung, the only less-than-trivial increase in black lung disease was in underground miners in Central Appalachia (ibid.). Small underground coal mines were singled out as having five times the rate of black lung compared to large mines, especially in Appalachia. Oddly, x-ray images of surface coal miners showed an unexpected incidence of silicosis along with some observations of black lung. (Laney AS, Attfield MD. (2010) : Coal workers' pneumoconiosis and progressive massive fibrosis are increasingly more prevalent among workers in small underground coal mines in the United States. Occup Environ Med. 2010 Jun;67(6):428-31. doi: 10.1136/oem.2009.050757.) It was the news reports which made a big deal out of the relative increase in black lung cases, not NIOSH.

Frankly, it's a mess. Only time will tell if the black lung death rate catches up with the NIOSH screened-miner black lung symptoms data. Given the problems with the black lung incidence rates, using the death stats as a surrogate has great appeal. The death stats have none of the problems that the rate data have. The virtue of death statistics is their simplicity. There is usually no second-guessing or doubts of biasing with death stats. The screened-miner data is really a mess in comparison. While I'm not completely sure that someone was wrong on the internet, it is more than certain that someone was confusing!

Pushing the data around masks the ongoing tragedy of black lung disease. While an increase in cases for the whole country is debatable, there is data to support that black lung cases in Central Appalachia and in small underground mines really have increased. Black lung disease in this country greatly decreased after 1970 because of the regulation of coal dusts that started in 1969. This is a clear cause and effect relationship between regulation and desired result. If the coal dust regulations are faithfully followed, black lung cases become increasingly rare. The tragedy here is that black lung is one of the truly preventable occupational diseases. Arm waving about data trends and variability will not make the black lung "hot spot" in Appalachia go away - only better enforcement of the coal dust regulations will do that.

You may want to note that the Mining Safety and Health Administration's budget for coal mine inspection and safety enforcement has been steadily cut for the last two decades so enforcement of the coal dust regulations is now uncommon compared to 30 years ago.

Anyone can play with the black lung data compiled by NIOSH at: (accessed 3/21/13).