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Fluoridation is ordered into water by Boards of Health in the US using their police powers. This happens despite the fact that if any wants to consume fluoridated water, it's cheap and easy to do so while the water 'filters' that actually reduce fluoride are expensive and generally ineffective.
I depended on a high quality water filter for drinking water for decades. It may have reduced the fluoride a little, but it did not eliminate the fluoride and was insufficient to protect my health. I accepted diagnoses of arthritis, IBS, chronic fatigue syndrome, etc. not realizing I was being fluoride poisoned. Those decades of misery ended quite quickly when I switched to bottled water, even to brush my teeth.
Moreover, some people shouldn't even shower in fluoriated water as liquids that go on the skin, also go in the skin. I know of people who have spent thousands on whole house systems only to find them ineffective for their needs. I know of others who have rigged up complex systems for bathing and still need to time their showers and change their filters much more often than recommended in order to prevent their symptoms of fluoride poisoning.
There are obstetricians, pediatricians, thyroid doctors, kidney doctors and oncologists who recommend their patients avoid fluoridated water. Hence, artifical fluoridation of water supplies is a immoral medical mandate that harms many consumers, especially the poor.
Water, like air, is necessary to life. Safe water, like safe air, should be safe for the most vulnerable among us. Therefore, the appropriate government model should be the one adopted in many places that ban public smoking.
sirpac - you seem to have a blinkered view of this research when you say it shows a "very evident significance between boys and girls exposed to fluorides."
You miss the whole point - it showed a difference in the IQ of boys and gurls for both fluoridated and unfluoridated groups. There was no statistically significant difference due to fluoridation. This is the data from Table 1 in the paper:
rs5526 - how can you say they concluded differently when the data in my table is taken from their table 1?
As for their regression analysis (which explained only 1.2% of the IQ variance), you are completely wrong. They did not say:
"for every 1 mg per day increased intake of F in pregnant expecting mothers, there was a 3.2 IQ drop for both boys and girls."
What they actually said was:
“A 1-mg higher daily intake of fluoride among pregnant women was associated with a 3.66 lower IQ score (95% CI, −7.16 to −0.14) in boys and girls.”
I will reproduce below my lengthy explanation that I provided for Bill on my blog (he was having trouble understanding the statistics and I guess you are too).
1: I hope you are familiar with what the word “associated” mean. Particularly it should not be seen as some sort of proof of a cause.
2: Again, it does not say the coefficient (the slope of the best-fit line) was exactly 3.66. It quotes a 95% confidence interval – in this case –7.16 to -0.14. There is a 95% chance the coefficient will be in this range.
3: That is the coefficient – but what would be the associated IQ values be. [Please don’t forget the word “associated” – it is too easy to fall into sloppy thinking and assume we are talking about an actual loss caused by fluoride. That has not been shown at all.]
The figures relate only to the best-fit line and that range of coefficients indicate values for the best-fit l line at a MUF of 1.0 mg/l in the range of 103.13 to 110.15. (I am using a constant for the line of 110.29 estimated from the paper’s Figure 3).
4: Now here is the figure with the data points taken from the paper.
5: Please note that the values from the quoted statement refer only to the line. 110.29 – 3.36 = 106.93 = the IQ value of the line at MUF of 1 mg/ml. The CIs indicate a 95% probability of values in the range 103.13 to 110.15. That range is indicated by the grey shade showing the place the best-fit line could occur.
6: Now note that at a MUF of about 1 mg/L ACTUAL values occur WELL OUTSIDE THAT RANGE. At least 74 to 140 or even wider.
In fact, only a few of the values are in the range given in the quote.
This tells you that the reported association [“3.66 lower IQ score (95% CI, −7.16 to −0.14)”] is an extremely poor predictor of what the actual values associated with the MUF will be.
7: The reason should be obvious. The data has a very wide scatter and the fitted line explains only a very small percentage of the true IQ variance. This can be calculated from a normal regression analysis – the authors would have the figures but did not report them. I think that is a major deficiency in this paper and similar papers from this group.
My estimate (using digitally extracted data from the figure) is that the reported association explain only 1.2% of the IQ variance. That is a very small value.
8: Finally, the author’s rely on p-values (in this case 0.04) and conventionally we say a relationship is “significant” if p is less than 0.05. By itself, p-values can be very misleading – and are often simply used to confirm a bias. Authors should provide all the information for their statistical analyses. In this case, an R-squared value of 0.012 (which they refused to report) would have told us that the reported association was meaningless. I do not think this is honest or objective reporting by these authors
On the other hand, I must compliment them for at least showing us some of the data – this enables us to come to our own conclusions about the significance of their results. Of course, anti-fluoride activists will only be discussing the p-value in their misrepresentations.
Bill, did you read my explanation of the statistical analysis in Green et al's paper? Did you understand it?
Do you agree the observed correlation is extremely flimsy?
Perhaps more could be said about the MUF analysis for boys. The residual plot suggests the assumptions inherent in a linear regression are suspect. The residuals are improved by a log transformation but I think this just adds to my point that this reported association is meaningless.
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