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Slinging mud at El Diablo A couple of months ago, on this blog we discussed a study by Busby and Messiers  entitled ‘Cancer near Trawsfynydd Nuclear Power Station in Wales, UK: A Cross- Sectional Cohort Study”, and which had a couple of shortcomings…or, in other  words…it was pretty bad. If you want to know why and how, have a look at “The  Story that wasn’t one…” (link).  The paper was published in ‘Jacobs Journal of Epidemiology and Preventive  Medicine’, and if you have never heard of that…you are still not the only one! It is  one of those new open access journals that features prominently on Beall’s list of  predatory, or in other words if-you-pay-we-will-publish-anything, journals (link).  What was also slightly dodgy about that whole study was that one of the authors  (Busby, for the record) is on the journal’s Editorial Board, which may well have  helped getting a paper with such errors published.  Anyway, that was a previous blog post and if you are interested in it you can find the link above (or elsewhere on The Fun Police site).   Interestingly, the same author and Editorial Board member has recently published a  new study in, of course, the same journal. It was published online on September 14,  2016, and as such got published within two months of submission of the first draft.  This implies it was an exceptionally well-written and scientifically accurate first  version of the paper, the peer-review and journal are very fast, or the peer-review  may not have been up to scratch…or of course a combination of the three. It is open  access, and you can find it by clicking here (link).  Enfin The paper is entitled “Is there Evidence of Adverse Health Effects Near US Nuclear  Installations? Infant Mortality in Coastal Communities near The Diablo Canyon  Nuclear Power Station in California, 1989-2012”. That is quite a mouth full, but in  summary the study looks at whether there is a correlation between living on a  coastal side close to the Diablo Canyon Nuclear Power Station and the number of  children dying in that area.   The idea behind the study is not that bad. Some previous studies have shown that  living close to a nuclear power station may be correlated with a number of  increased disease risks, and some have proposed that releases from the nuclear  power station may have something to do with that. Busby in this study argues that  this risk is an underestimate of the true risk because whereas previous studies  looked at population generally living within the vicinity of power stations, the real  risk comes from contamination down-wind and down-stream of those stations.  Specifically for power stations built near the sea, which is quite a lot of them, this  would result in contamination of coastal areas with radioactive materials.   Indeed, as you may have guessed, Diablo Canyon Nuclear Plant in California is such a  place.  So, Busby got the 1989-2012 births and infant deaths by year and by Zip code,  summed them per area (zip code group) and divided the deaths by the births to  obtain crude mortality rates per 1,000 births. For unknown reason to me, since he  had the annual data, he then decided to group this again in four 6-year periods.  And then, finally, he divided these in a Coastal Group, which supposedly has the  highest exposure, an Inland Group, which has lower exposure but is still relatively  close to the power station, and as a third group he used the average numbers across  all of California. The paper comes with a nice figure which can be used to draw the  coastal and inland groups of zip codes, so well at least you can see what was done.  Interestingly, there seem to be two sets of Inland “control areas”, which is a bit  unclear, while also not all coastal areas have been included. This is all a bit  ambiguous, but let’s give him the benefit of the doubt. What first springs to mind when looking at the infant mortality rates is, quite  worryingly, how high they are compared to European countries (i.e. 4-7 per 1,000  compared to 2-4ish in Europe as you can see here). That however, is a completely  different story… The general argument in the paper is that despite the fact these rates have gone  down in the coastal areas, the inland areas, and in California as a whole up to the  year 2000, they kept going down until 2007-2012 in California while in the Coastal  areas they started to increase again post-2000. In the “intermediate” inland areas  the rate staid fairly stable at about 4 per 1,000 from 2,000 onwards. Busby’s  argument then is that this had to have been the result of the releases from the  nuclear power station, and he shows a nice and clear linear correlation between the  infant mortality rate in the coastal areas in the four periods with the cumulative  amount of tritium (as a marker of all releases from the plant) from the start of the  operations in 1986..  This, in a nutshell, is the story.   Could it be true? Yes it could.   Is it a bit flimsy? It most definitely is.  Despite the fact Busby had the data to look at this correlation on an annual level,  thereby being able to look at temporal patterns in a much better way, he choose not  to, and instead used four periods only. I personally find that odd. Moreover, there is a table in the paper which shows the number of births in each of  the included areas (stratified by coastal, inland or California as a whole) for the four  periods. After just one glance you should notice that whereas in all but one of the  coastal areas the number of births has steadily been decreasing over the time  period, in most of the inland areas this has been increasing; in fact, the overall  decrease is almost entirely the result of a mass exodus in the area of San Luis  Obispo. Let’s think about that a bit…. ….so in the coastal areas the population has decreasing by about 20% over that time  period, while in the inland areas it has decreased by less than 5%. If the number of  infants dying remained more or less stable (for sake of argument), the observed  changes in rates would be exactly as shown in the figure – as a result of demographic  changes only!   Is that likely? Well that depends who is actually leaving (or entering) the areas.  Indeed, although I don’t know the areas it seems very plausible that in the 24 year  period much will have changed. Someone else pointed that out and said it was the  result of Busby not taking the changes in Hispanic/White birth rates in the study  area into account because, apparently, the infant mortality rate in Hispanics is  higher. Busby shows the percentages of Hispanic births over time in the three areas  and shows that this cannot be the explanation (conveniently, this is not aggregated  by the same time periods, so cannot be directly linked). This could have easily been  modelled statistically, such that the effect of the percentage of Hispanic/White  births was taken into account (and what about a, presumably, increased percentage  of mixed-race births? How would that change the estimates?). This is called  multivariate regression methods, and if you don’t know what this is; for the purpose  of this article it is easiest to remember that it is easy to do and Busby has access to  the data to do this.  There are only very few reasons why someone would not do multivariate regression  models to adjust for important confounding factors (for confounding click here):  1)  It is the start of the data analyses, and a researcher just wants to see       what is going on at face value. Multivariate analyses will follow after.  2)  Multivariate analyses are not needed because it is a randomized  controlled trial and all confounders are randomly distributed in the groups  (note: this is NOT the case here). And even then it is often done anyway, just in case. 3)  You want to make a point, and it shows very clearly in straightforward  comparison, but not when you to better, multivariate analyses. Indeed, point three is dodgy science, and problematic! I talked about this in my last  blog post as well (reference to “Alcohol and ‘fact’ checking in Ireland” here). It  unfortunately happens a lot; most notably because it often fits so nicely with the  point we are trying to make, so why look further…this is called confirmation bias.   And I strongly suspect this is what has been done in this paper too. A point well  made, so why spoil it by making it more complicated?!?!  Busby has the annual data and he has the racial demographics of the births (at least  at area level), so why not look at it?   But lets go back to those differences in migration rates (remember, the 20% vs 5%)  and think about multivariate analyses a bit further. Say for example that it is mostly  young people moving out of the coastal areas and into the inland areas, what would  happen then? The result would be that with less births (we know that from the  paper), and with infant mortality rates going up (we know that from the paper too),  that the percentage of birth to older parents probably has gone up too. Maternal  age is directly related to infant mortality rate in the US (here is a link to a paper for  the US (link1 and link2), so that could explain what we are seeing in the paper as  well, and therefore we may not need the contamination from the nuclear plant as a  factor to explain the observed patterns. Can we find out? I did a quick online search, and was not able to quickly find longitudinal data  (please let me know if you know how to get this), but the 2010(ish) median age for  each of the areas in the study is easy to find. That will work just as an indication…  So the range for the coastal areas is about 28 to 57 years of age (median ~44 years),  and for the inland areas the range is about 27 to 45 years (median ~37 years). In  other words….yes indeed, the population in the coastal areas is older on average  than the inland area (at least in the 2007-2012 time stratum), and this could explain  the observed differences. My point here is not that the cause for these differences in infant mortality rates is  definitely not radiation exposure, I don’t know that (although in my opinion this is  highly unlikely), but that with relatively little effort I found a possible other  explanation. And it is fairly straightforward to come up with possible other  demographic factors as well; for example, maybe socio-economic status differs  between the areas, or income (in fact, the same sources showed that in 2010ish  median household income was comparable in both areas, and was about 45k, but  was much higher in California as a whole (58k) which may explain that difference).  And maybe, if these are poorer areas, there is less investment in healthcare or more  people will have less access to healthcare, just to name some other things.   My main point is that all these other factors could easily have been included in the  analyses, and these data are all available (probably for free). Just to recap; there  are three reasons why you would not do multivariate analyses when the data are  available…  …..and only one seems relevant to this particular study.    As a side note, since I came across this when doing a bit of googling about the area,  and thought it would be nice to illustrate the correlation with contamination issue  as well. How about the following alternative hypothesis?  I came across the following report (full report link) entitled “Pismo Beach Fecal  contamination source identification study”. Pismo beach is one of the coastal areas  in the study, and the report describes a study to identity the biological sources of  fecal contamination as well as the physical and environmental factors that influence  the levels of bacteria in the ocean waters at Pismo Beach, and was conducted in  2008 (you know, in the final 2007-2012 time block). So contamination must have  been ongoing in the preceding years….I mean, you know, …..swimming in water  polluted with fecal residue, let alone swallowing it… just sayin’…  * Anyway, personally, I don’t think studies like this should be published (or at least not without properly addressing their shortcomings); especially not in a highly emotive  area of environmental health such as this one. There are enough worries related to  radiation and nuclear power as it is, and these things don’t help.   A comparable worry has been ongoing in the UK in relation to childhood leukaemia  clusters in areas surrounding nuclear power stations, and whether this could be the  result of the release of radioactive contamination from the sites. The UK  Independent Advisory Committee on Medical Aspects of Radiation in the Environment (COMARE) has conducted a thorough and exhaustive review of all available evidence  and concluded that a more likely explanation, at least in part, is the result of  urban/rural population-mixing and associated changes in viral loads (disclaimer: I  am a member of that committee). If you are interested, a link to the full report  which can be downloaded for free, can be found here (link).  
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