Category Archives: Health and Medicine

Scientific integrity requires critical investigation – not blind acceptance


Some people seem to want to close down any critical discussion of the current research into the relationship between water fluoride and child IQ. They appear to argue that claims made by researchers should not be open to critical review and that the claims be accepted without proper consideration of the data and evidence.

Anti-fluoride campaigners, of course, argue this way any time the research they promote is questioned. After all, they have a bias to confirm and an ideology to support and rely on claims that often don’t stand up to proper consideration. I expect that, but I am concerned to hear these arguments from scientific reviewers of this research.

In the video above Dr William Ghali of the Canadian O’Brien Institute for Public Health counters critiques of some research with the comment: “the studies can’t be undone and they can’t be unpublished.”

Of course they can’t – but they can, and should, be critically considered – not blindly promoted as the best things since sliced bread. Critical consideration is, or should be, the normal scientific reaction to newly published studies.

Dr Ghali is one of the authors of a recent review of the science around community water fluoridation, COMMUNITY WATER FLUORIDATION: A REPORT FOR CALGARY CITY COUNCIL. He made the above comment during his presentation to a recent meeting of the Calgary City Council – the video above contains a section of his presentation (selected and promoted by the Fluoride Action Network (FAN) an anti-fluoride activist organisation).

I am amazed at that comment – and other comments of his. I could understand if he was responding to the research critiques by explaining where they were mistaken or misinterpreted the evidence – we should always consider the factual evidence in our scientific discussions. But he seems personally upset that anyone should pursue a normal scientific critical discussion. He admits to getting angry at:

“The notion that you can just talk away 10 years of research.”

And

“I respect the doers of the research and the deliverers of the evidence and don’t think they should be shot for tough messages.”

Yet he himself is denying respect to scientists who critically discuss research (by authors that he appears to be protective of) and is attempting to “shoot” down researchers who discuss the problems in that research. He accuses scientific critics of attempting to  “simply sweep aside scientific findings because one disagrees with the results.” Yet he attempts to “sweep aside” the normal scientific critique of research – rather than deal specifically and factually with the criticisms themselves.

Sometimes it is necessary to “talk away 10 years of research” if the critical scientific consideration of the research findings show them to be faulty. We are talking about science – not religion.

Nothing sacred about scientific findings

There is nothing sacred about scientific findings – they are and always must be open for critical consideration and critique. Publication in a reputable journal and inclusion of big names in the author list is no guarantee of good science. And all scientific findings must be considered as provisional – most of what is considered factual in science often turn out to be wrong, at least in part. This is how science progresses and critical analysis and scientific critique of published work is key to that development.

Critique of published research is vital and it should never be ignored, “swept aside” or discredited by saying things like “once published it can’t be unpublished” of referring to critiques as “sweeping aside because one disagrees.” Good scientific critique is not swayed by authority or author’s claims but looks at the data, findings and interpretations – critically. It is not an evidence-free “sweeping aside.” In a good-faith open scientific exchange, the response to criticism should be the same.

Having said this I can understand a little of what is driving the two people in their comments in the above video – comments that are critical of scientific commenters but ignore the way the anti-fluoride movement has misused and misrepresented this research. The O’Brien review they were authors of was roundly criticised for its weaknesses when it was made public. On the other hand, the anti-fluoride advocates lavished it with praise – for these very same weaknesses.

Scientists are human (actually very human) and, of course, sensitive to criticism. Even the best scientist will often react defensively and attempt to discredit critics rather than deal with the contents of the criticisms.

Misrepresentations

The only time Dr Ghali gets at all specific in this video section is in his criticisms of the letter sent by 30 academic and health experts to the US National Insitute of Environmental Health Science (NEHS) about the study (see Experts complain to funding body about quality of fluoride-IQ research). This letter expressed concern about the study recently published by Green et al (2019) listing a number of specific scientific limitation of the study (see If at first you don’t succeed . . . statistical manipulation might help and Experts complain to funding body about quality of fluoride-IQ research)  The letter also expressed concern about the poor statistical reporting of the data and lack of transparency regarding methodology.

After listing ten scientific concerns the experts made a specific request:

“We urge NIEHS to ask the Green authors to release their RIF data set and provide a thorough explanation of their analytical methods. Doing so could enable an independent review that would bring clarity and ensure the scientific record is accurate.

Should the Green researchers not voluntarily release their data, please advise us on what the process would be to have the data set released so an independent analysis of the Green data can be conducted.”

But this is how Dr Ghali specifically commented on this important expert’s letter:

“Twenty or so North American academics [actually 30 North Americans and experts from the UK and Australia] wrote to the NIEHS denouncing the recent Canadian study critiquing it on many levels  Making assertions the team at York University refused any access to their data and their refusal to permit reanalysis and they are not being transparent. The allegation is false. The authors are in fact  in an active process of discussing with health Canada a Teflon bias-free process of making the data available for a secondary analysis. And again, there is one thing that gets under my skin are assertions, attacks on messengers.” [My emphasis]

Come off it. On the refusal to make data available the expert’s letter mentions only:

“In recent weeks, at least two of the Green authors have declined to respond affirmatively to requests from other researchers for access to the data and analytical methods they used.”

It did not “denounce” the study (scientific critique is not “denouncing”), and it definitely did not assert the whole team was refusing any access. It simply pointed out that no one at that stage had reacted positively to the request for access to the data. That is not, as Dr Ghali claims an “attack on messengers.” Nor is it, as he claims, a “false allegation.”

A respectful and scientifically ethical response to the expert’s letter would be for  Dr Ghali to consider and respond to the list of ten limitations of the study described in the letter. But instead, he has misrepresented the letter and made a false allegation himself regarding the request for access to data.

Where is the scientific integrity in that?

As an aside, I am a bit cynical about the authors’ claim that they are “discussing with health Canada a Teflon bias-free process of making the data available for a secondary analysis.” Dr Ghali appears to be in more intimate contact with the authors than the rest of the scientific community because this is the first I have heard of the authors’ response. But I fear the “Teflon bias-free process” referred to may, in the end, be a bureaucratic solution which makes the data available to only a select “trusted” few for their presumed approval.

The problem of transparency

Dr Ghali also misrepresents the letter by claiming it accuses the authors of lack of transparency. Yes, it expresses concern about the lack of statistical and methodological information but refers to this as a general problem in scientific publications, particularly where statistical analyses are involved. It even cites a published paper on this (Prager et al. 2019: Improving transparency and scientific rigor in academic publishing. Brain Behav. 9(1): e01141).

Another example relates to reliance on p-values:

“The American Statistical Association has established six principles on the use and analysis of p-values, one of which states: “Proper inference requires full reporting and transparency.” By releasing the data and a detailed explanation of their analytical methods, the Green authors would enable the scientific community to better assess whether their choice of p-value was appropriate.”

All this is simply part of a good-faith scientific critique which should be normal in science and should never be squashed or prevented. Remember Ghali himself said messengers should not be shot for delivering a message.

But if we are to discuss the problem of transparency I am really concerned at the unwillingness of the authors, and their scientific defenders, to participate in a free good-faith scientific exchange on their findings.

I guess they can not be blamed for promoting their own research while being silent about its limitations, or for the fact that the journal which published their paper has a policy of not publishing any critiques of published paper after 4 weeks. But why should they promote their findings on social media but refuse to enter into any discussion on it?

For example, Rivka Green, the first author of the paper, opened a Twitter account where she promoted the paper. But when some discussion of the limitations started she withdrew and closed the account down.

In another example, a biostats PhD student at Pittsburgh university was making some general comments about the data in the Green paper on Twitter soon after its publication. But two of the authors approached his university department and supervisors and he was forced to delete his tweets. (This is information from the student  himself who is wary about going public because of this unpleasant exposure to academic politics and he is unsure of the consequences of making further comments).

I have had personal experience of the lack of transparency by Dr Chrsitine Till’s group (involved in the study reported by Green et al. 2019) and its supporters. My own critique of one of the early papers from the group (Malin & Till 2015) was denied consideration for publication in the publishing journal by the Chief Editor, Prof Grandjean, who publicly identifies with the group and the anti-fluoride movement (see Fluoridation not associated with ADHD – a myth put to rest). My critique was eventually published in another journal: (see Perrott 2018: Fluoridation and attention deficit hyperactivity disorder a critique of Malin and Till (2015). British Dental Journal, 223(11), 819–822). Christine Till is aware of this critique but purposely ignores it whenever she or her coauthors cite Malin & Till (2015) in their publications (see, for example, ADHD and fluoride – wishful thinking supported by statistical manipulation?).

And what about the lack of transparency displayed by Dr Ghali himself. He misrepresented the expert’s letter – but was also very selective in referring to other reviews of this study. For example, in the video above, he mentioned the CADTH (Canadian Agency for Drugs and Technologies in Health) review  on possible neurological effects of fluoride which was very critical of the Green et al. (2019) paper and quotes from two sections of the review which said:

“The evidence is weak due to multiple limitations  . . ” (p 5)  and “further well conducted research is needed to reduce uncertainty. ” (p 14)

But he ignores completely a more damning statement in the CADTH review which says:

“The study by Green et al., 2019 concluded that “maternal exposure to higher levels of fluoride during pregnancy was associated with lower IQ scores in children aged 3 to 4 years.” (p. E1) This conclusion was not supported by the data” (page 12)

A disclaimer

I am very conscious that I have relied on only one section of Dr Ghali’s presentation to the Calgary City Council. And that this section was cherry-picked by FAN to present him as an ally in their anti-fluoride campaign. I have not had the time to look at the full video of his presentation yet – it is available on YouTube: Dr. Ghali (O’Brien Institute) – Full Calgary Presentation on Fluoride. However, I think the comments made on this specific section of his presentation stand by themselves and needed a response.

Dr Ghali may well have made criticisms of the misrepresentation of this research by FAN, by the anti-fluoride campaigners also presenting to the Calgary City Council and by anti-fluoride campaigners in general. After all, FAN, which made the selection for this video and is promoting it is hardly likely to include such criticism.

So to be fair to Dr Ghali and to support the proper good-faith scientific exchange I am talking about I will email him and offer him the right of reply to this article.

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Anti-fluoride propagandists appear not to read the articles they promote

Anti-fluoride activists are rubbing their hands in glee over what they claim is “yet another study” showing fluoride harms the brains of children. But their promotion relies on IQ relationships which the paper’s authors acknowledge disappearing when outliers or other factors are considered. And they completely ignore other relationships which indicate much larger effects and are not influenced by outliers or other factors.

Why ignore this gift from the paper? I can only conclude these anti-fluoride campaigners don’t actually read the papers they promote.

Mind you, the paper is rather confusing. But the data, relating to formula-fed infants, is hardly surprising. It’s from the same group that has produced multiple studies along the same line – and suffers from the same weakness the other studies do.

The paper citation is:

Till, C., Green, R., Flora, D., Hornung, R., Martinez-mier, E. A., Blazer, M., … Lanphear, B. (2020). Fluoride exposure from infant formula and child IQ in a Canadian birth cohort. Environment International, 134(September 2019), 105315.

Multiple parameters measured

This research group appears to be taking the approach of searching existing databases using multiple parameters – in the hope of finding significant differences or trends. And then interpreting significant trends as evidence for a cause.

The problem is that the p-value, used to judge significance, gets pretty meaningless when multiple attempts are made on the same data like this, although there are statistical procedures for correcting the final p-values to get a more meaningful measure. But, more importantly, p-values are pretty meaningless even at the best of times – remember correlation is not evidence for causation and should never be used that way.

Don’t be fooled by a statistically significant relationship. Low p-values, even high R-squared values should not be used as evidence of causation. Data from Spurious Correlations.

The parameters used in this paper are full-scale IQ (FSIQ) verbal IQ (VIQ), performance IQ (PIQ) for the cognitive parameters. Fluoridation, water fluoride concentration and estimated fluoride intake were used as the fluoride parameters.

Looks very much like they were “spreading their bets” by using a range of parameters.

Difference between breast-fed and formula-fed infants

The difference they reported between breastfed (BF) and formula-fed infants are unsurprising. These differences were only significant for maternal education, HOME total score (a measure of the child’s home environment), full-scale IQ (FSIQ),  verbal IQ (VIQ), water fluoride concentration and estimated fluoride intake. Parental attitudes to breastfeeding are probably expected to change with education. Breast-feeding has previously been reported to result in children with higher IQs and, however poor the F intake estimate, it would obviously be higher where the water is fluoridated.

IQ difference between fluoridated and unfluoridated areas

The data extracted from the paper’s Table 1 also shows that the VIQ of breastfed children is statistically higher in fluoridated areas. and the PIQ of formula-fed children is statistically higher in non-fluoridated areas. There were no statistically significant differences in FSIQ between children in fluoridated and unfluoridated areas irrespective of the feeding method.

So, just using the mean values of the cognitive measures there were no statistically significant effects of fluoridation of FSIQ, VIQ for formula-fed children and PIQ for breastfed children.

However, child VIQ was greater in fluoridated areas for breastfed children and lower in fluoridated areas for formula-fed children.

Digging difference out of trends

The authors repeat the same method they used in Green et al., (2019) where although mean values showed no effects of fluoridation they used trends from linear regressions to imply there were effects (see If at first you don’t succeed . . . statistical manipulation might help).

In all their promotion of the paper, the anti-fluoride campaigners refer only to the data for FSIQ – the figures they cite make clear that their term IQ is referring solely to FSIQ. This is strange as the story is very poor for FSIQ and I would have thought they would concentrate on PIQ where effects are larger and not influenced by others or other factors as FSIQ is.

But, let’s look at the FSIQ story – at least the paper provides a figure enabling extraction of data for an independent analysis. The figure below is from the paper’s Figure 1 showing the relationships of FSIQ to drinking water fluoride concentration for breastfed (BF) and formula-fed (FF) babies.

The paper claimed a statistically significant (p<0.05) relationship for formula-fed (FF)(B=-8.80 [-16.34,-0.92]) but not for breastfed (BF) babies.

I can’t get the same result with the data I digitally extracted from the figure (about 90% of data points) The extracted data showed for For FF: B=-2.07 (-11.17, 7.04), p=0.6 and for BF B=1.92 (-6.74, 10.59).

The difference surprises me. I am aware I was able to extract only about 90% of the data points (n=354 compared with the paper’s 398) but would not have expected such a large difference in regression result. I would dearly like someone to duplicate this as a check.

These were the results I obtained using the data I digitally extracted from Figure 1 in Till et al., (2020).

Perhaps this is just another indication that the relationship is very weak.

But, Importantly, the IQ relationships were not significant when outliers and covariate effects were included. Till et al., (2020) found the significant relationship they reported for FF disappeared when the 2 low IQ outliers were removed. The regression without the outliers was B=-6.28 (-13.98, 1.42), no p-value given. The relationship also disappeared when maternal urinary fluoride (MUF) was included as a covariate. But before I get the response this proves that MUF, rather than FF is the determining factor, Till also reported (in the supplementary data) that “MUF was not significantly associated with FSIQ score (B = -0.54, 95% CI: -3.04, 0.90, p = .28).” 

Why concentrate on FSIQ instead of PIQ?

The authors include only a figure for FSIQ plotted against water concentration. This seems strange as the quoted relationship disappears when the 2 outliers are excluded or covariates included in the regression.

In contrast, the relationship of PIQ with water concentration is statistically significant for both breastfed and formula-fed infants and remained significant when controlled for maternal; urinary fluoride or when the 2 outliers are removed. (The authors could not find any statistically significant relationships for VIQ). The effect size was also much larger than that found (before adjustment) for FSIQ.

Whereas the B value for the relationship of FSIQ with water fluoride before adjustment was -8.8 IQ points per 1 mg/L water fluoride the equivalent coefficients for PIQ were:

Formula-fed children: B=-18.52 (-27.54, -9.52);
Breastfed children: B=-12.38 (-20.90, -3.88)

These B coefficients were a little smaller when adjustments were made for the 2 outliers or for maternal urinary fluoride but they were still statistically significant (p,0.05).

So, why no figure for PIQ vs water fluoride? I would have thought such a figure would be far more important for the paper than the included one for FSIQ (where the relationship became non-significant when adjustments were made). Data extracted from a PIQ figure would also have enabled determination of how much of the PIQ variance was explained by water fluoride. Although, the very large 95% confidence interval range suggests to me that very little of the PIQ is actually explained by water fluoride. I think the strange data presentation may have been a result of attemtps to confirm a bias.

The anti-fluoride people have talked about IQ (meaning FSIQ) rather than PIQ in their promotion of the study. Perhaps they have not actually read the paper. They seem not to realise that the relationship they rely on disappears when considered properly.

And it is not just a convenient shorthand. For example, The Fluoride Action Network press release says:

“A study published this week found a large decrease in the IQ of children who had been fed infant formula reconstituted with fluoridated tap water, compared to formula-fed children living in unfluoridated areas. The study by a research team based at York University, Toronto, followed a large cohort of Canadian mother-child pairs through age 3-4 years and found an average drop of over 4 IQ points for children in fluoridated areas.”

The local Fluoride Free NZ (FFNZ) press release also makes clear they are referring to FSIQ:

“children lose 4.4 IQ points for every 0.5 mg fluoride added to their drinking water if they are formula-fed rather than breast-fed.”

They clearly refer to the FSIQ relationship where B=-8.8 (or -4.40 for  0.5 mg/L water fluoride concentration).

Did these anti-fluoride people not get past the paper abstract or the press release put out by the authors?

Fluoride intake

The study also included a calculated measure of fluoride intake from formula. The calculation is questionable and was not significantly related to VIQ or FSIQ. However, the relationship with PIQ was significant even after adjustment – although the large 95% confidence interval suggests it did not explain much of the variance in PIQ. I digitally extracted data for the PIQ – F intake figure (their Figure 1B) and regression analysis indicated F-intake explained only about 1.5% of the PIQ variance. (Unfortunately, I was unable to extract more than 78% of the data as a large number of data points seemed to coincide. This is probably inevitable with the method they used to estimate F intake).

Problems with the Till group

I find it interesting that the authors specifically express their coefficient B values for 0.5 rather than the normal 1.0 mg/L water fluoride because they wish to relate their relationship to water fluoridation. They write:

“To aid interpretation, we divided all regression coefficients by 2 so that they represent the predicted IQ difference per 0.5 mg/L of fluoride in tap water or 0.5 mg fluoride from formula; 0.5 mg/L corresponds to the approximate difference between mean water fluoride level in fluoridated versus non-fluoridated regions in our sample.”

This suggests to me the group has a preoccupation of finding fault with community water fluoridation.

Mind you, I had already come to this conclusion because, when taken together,  papers coming from this group report relationships that are always weak and very often contradictory. If a relationship isn’t significant when maternal urinary fluoride is used, they switch to water fluoride. If that is not successful they use a non-verified fluoride intake measurement of their own invention. They seem to be searching for any relationship which will confirm their bias.

The truth is that in these and similar studies the data is often not very good (no one should be using spot urine F tests for example) and the relationships found are always very weak. The results are usually confused because of the different parameters used and also the results are often contradictory. For example, data will sometimes show an increase in IQ with fluoridation or drinking water fluoride (see the table above, A conference paper on the maternal prenatal urinary fluoride/child IQ study has problemsWhat do these mother-child studies really say about fluoridation?, and The anti-fluoride brigade won’t be erecting billboards about this study).

The authors also seem to be very willing to make exaggerated claims linking their weak results to health policies and often seem to work in collusion with some anti-fluoride activist organisations and people. For example, Bruce Lanphear, one of the coauthors on the Green et al., (20219) paper, is serving pro bono as an expert witness for the Fluoride Action Network and other antifluoride groups in a current legal case.

Despite this apparent bias and the weakness of the data in these papers, they should stand on their own merit instead of the reputation (good or bad) of the authors. It is up to interested readers to critically examine the data and make their own decisions about the reliability of the claims being made.

However, this does require readers to actually read the papers and think critically about them. It appears to me that most anti-fluoride campaigners never do this but simply rely on newsletters and press releases coming from “Head Office – the Fluoride Action Network.

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The anti-fluoride brigade won’t be erecting billboards about this study

If FFNZ really put their faith in “Top Medical Journals” they would now be amending their billboards to recognise new research results. Image from FFNZ but updated to agree with the latest research.

Just imagine it. If the local anti-fluoride group Fluoride Free NZ (FFNZ) really put their faith in “Top Medical Journals” they would now be erecting billboards encouraging pregnant women to drink fluoridated water because a new study shows a positive relation of child cognitive abilities with prenatal maternal urinary fluoride.

The study has been reported at a recent conference – this is the citation and links to an abstract:

Santa-Marina, L., Jimenez-Zabala, A., Molinuevo, A., Lopez-Espinosa, M., Villanueva, C., Riano, I., … Ibarluzea, J. (2019). Fluorinated water consumption in pregnancy and neuropsychological development of children at 14 months and 4 years of age. Environmental Epidemiology, 3.

This appears to be research using Spanish data and the abstract reports that a number of cognitive measures for children aged 4 – 5 years-old are positively related to their mother’s prenatal urine fluoride concentrations:

“At the age of 4-5 years, an increase of 1 mg/l in the level of fluoride in urine during pregnancy (mean level of 1st and 3rd trimesters) was related to a higher score on the perceptual-manipulative scale of 4.44 (0.13, 0.75) points. Taking into account the window of prenatal exposure, at week 32 the level of fluorine was associated with an increase of 4.11 (0.28, 7.94) points in verbal function, 3.57 (-0.03, 7.18) in perceptive-manipulative and 3.97 (0.29, 7.65) in general cognitive.”

And the researchers concluded:

“Prenatal exposure at the levels found in fluorinated drinking water may exert a beneficial effect on the development at 4 years of age. At low doses, fluoride could present a dose-response pattern with a beneficial effect.”

Compare this with the report of a negative effect taken from the abstract of Green et al., (2019) – the study FFNZ relies on for their current scaremongering propaganda:

“A 1-mg/L increase in MUFSG was associated with a 4.49-point lower IQ score (95%CI, −8.38 to −0.60) in boys, but there was no statistically significant association with IQ scores in girls (B = 2.40; 95%CI, −2.53 to 7.33).” [MUFSG is an abbreviation for maternal urinary F cocnetration].

And Green et al., (2019) concluded:

“In this study, maternal exposure to higher levels of fluoride during pregnancy was associated with lower IQ scores in children aged 3 to 4 years. These findings indicate the possible need to reduce fluoride intake during pregnancy.”

So there you go. You can happily erect a billboard to promote either message depending on your own bias and your desire to confirm that bias. You can scaremonger and attempt to frighten mothers and pregnant women. Or you can do the opposite – perhaps even scaremongering to warn mothers that they must drink fluoridated water – or warn them not be taken in by activists who only wish to reduce your child’s opportunities in later life.

My take on this.

I have yet to see the full paper reporting this study and look forward to its publication. But I am not looking to confirm a bias – I simply want to see the data and subject it to the same sort of scientific critique I have made for the Green et al (2019) paper.

My initial response is that the reported relationship will be weak (going on the confidence intervals given). So I am sure that many of the criticisms I made of Green et al., (2019) will also apply to Santa-Marina et al., (2019).

But I think this situation with conflicting results from different research groups – both relying on weak statistical relationships – is the sort of result we can expect from analysis of unsatisfactory weak data. Sensible readers should be aware of this and not be swayed by single studies – especially studies using very weak relationships.

Unfortunately, activists do not have such scientific ethics – they will simply use the data and studies supporting their propaganda and biases. And they will claim these studies are of high quality and the best thing since sliced bread. On the other hand, these activists will attempt very hard to discredit the new study and I wonder if they will be able to see the irony of using arguments that could equally be used against the Green et al., (2108) study they promote.

More serious is the confirmation bias that goes on in the scientific community and the way authors like those involved in the Green et al., (2019) paper make statements promoting their work which are then used by activists to promote scaremongering messages.

I do not know enough about the research group involved in the Santa-Marina et al., (2019) study but, from their record, the other research group headed by Christine Till seem to be driven to confirm their bias against community water fluoridation and this is motivating them to extract weak relationship from poor data.

See my critiques of papers from Christine Till’s group:

Conclusion

I hope that this new study is reported in the media with the same interest the Green et al., (2109) study was. But I also hope the situation is used to get the message across that this sort of study should not be used to inform public policy. And that we should not be taken in by the scaremongering promotion of these sort of weak studies by anti-fluoride activists.

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ADHD and fluoride – wishful thinking supported by statistical manipulation?

Finding reality needs more than wishful thinking. The problem is that statistical arguments often provide a jargon to confirm biases. Image credit: Accurate Thinking Versus Wishful Thinking in Gambling

I worry at the way some scientists use statistics to confirm their biases – often by retrieving marginal relationships from data that do not appear to provide evidence for their claims. This seems to be happening with the recent publication of a study reporting on maternal urinary fluoride-child IQ relationships in Canada (see If at first you don’t succeed . . . statistical manipulation might help).

Now we have a new paper from this group of researchers that seems to be repeating the pattern – this time with fluoride- attention deficit hyperactivity disorder (ADHD) relationships. The paper is:

Riddell, J. K., Malin, A., Flora, D., McCague, H., & Till, C. (2019). Association of water fluoride and urinary fluoride concentrations with Attention Deficit Hyperactivity Disorder in Canadian Youth. Submitted to Environment International, 133(May), 105190.

At first sight, the data does not seem promising for the fluoride-ADHD story. Compare the values of some of the factors they considered for Canadian youth which have been diagnosed with ADHD with values for youth not diagnosed with ADHD (From Table 2 in the paper).

It seems that being a male and exposure to smoking in the home are two factors predisposing youth to ADHD (already known)  but the fluoride in tap water and fluoride intake (indicated by urinary F) have no effect. Although the data suggest that residence in sites where F is added to tap water may reduce the chances of ADHD diagnosis.

But the authors actually conclude that fluoride does increase the chance of an ADHD diagnosis. So it seems, once again, statistics appear to have been used in an attempt to incriminate fluoride – to make a silk purse out of a pig’s ear.

In effect, the paper is reporting three separate studies:

  • They looked for a relationship of ADHD diagnosis with urinary fluoride;
  • They checked if there was a difference in ADHD prevalence for youths living in fluoridated or unfluoridated areas, and
  • They looked for a relationship of ADHD diagnosis with F in tap water.

No relationship of ADHD with urinary fluoride

SDQ hyperactive/inattentive subscale scores were obtained using a Strengths and Difficulties Questionnaire. Information about ADHD diagnosis and SDQ ratings were provided by parents of children aged 6 – 11 years and from a questionnaire completed by youth aged 12 – 17 years.

The paper reports:

“UFSG [urinary fluoride] did not significantly predict an ADHD diagnosis (adjusted Odds Ratio [aOR]=0.96; 95% CI: 0.63, 1.46, p=.84) adjusting for covariates.”

Similarly:

“UFSG did not significantly predict SDQ hyperactive/inattentive subscale scores
(B=0.31, 95% CI=−0.04, 0.66, p=.08).

So no luck there (for the authors who appear to be wishing to confirm a bias). The tone of the discussion indicates the authors were disappointed  as they considered urinary fluoride has “has the advantage of examining all sources of fluoride exposure, not just from drinking water.” However, they did discuss some of the disadvantages of the spot samples for urinary fluoride they used:

“. . . urinary fluoride levels in spot samples are more likely to fluctuate due to the rapid elimination kinetics of fluoride. Additionally, urinary fluoride values may capture acute exposures due to behaviours that were not controlled in this study, such as professionally applied varnish, consumption of beverages with high fluoride content (e.g., tea), or swallowing toothpaste prior to urine sampling. Finally, the association between urinary fluoride and attention-related outcomes could be obscured due to reduced fluoride excretion (i.e., increased fluoride absorption) during a high growth spurt stage.”

We should note the WHO recommends against using urinary F as an indicator of F intake for individuals, and certainly against using spot samples (see Anti-fluoridation campaigner, Stan Litras, misrepresents WHO). They recommend 24-hour collections (see the WHO document Basic Methods for Assessment of Renal Fluoride Excretion in Community Prevention Programmes for Oral Health”). I really cannot understand why these researchers chose spot sampling over 24-hour sample collection – although this would have not overcome the problem that urinary F is not a good indicator of fluoride intake at the individual level.

While it is refreshing to see the disadvantages of spot samples for urinary fluoride discussed, this probably would not have happened if they had managed to find a relationship. Neither Green et al., (2109) or Bashash et al., (2017) considered these problems – but then they managed to find relationships (although very weak ones) for spot samples.

Relationship of ADHD diagnoses with fluoridation

While this paper reports a significant (p<0.05) relationship of ADHD diagnosis and SDQ ratings with community water fluoridation (CWF) this really only applies to older youth (14 years). The relationship is not significant for younger youth (9 years).

However, the relationship is rather tenuous –  this effect of age for ADHD diagnosis was seen only for “cycle 3” date (collected from 2012 to 2013) and was not seen for “cycle 2” data (collected from 2009 to 2011). The confidence intervals for Odd Ratios are also quite large – indicated the high variance in the data.

I think their conclusion of an effect due to fluoride and their lack of consideration of the poor quality of their relationships and alternative explanations for their results smacks a bit of straw clutching. The authors appear too eager to speculate on possible mechanisms involving fluoride rather than properly evaluating the quality of the relationships they found.

Relationship of ADHD diagnoses with tap water F

The paper reports a statistically significant (p<0.05) relationship of ADHD diagnosis with tap water fluoride. While the reported Odds Ratio appears very large (“a 1 mg/L increase in tap water fluoride was associated with a 6.1 times higher odds of ADHD diagnosis”) the 95% confidence interval is very large (1.60 to 22.8) indicating a huge scatter in the data. Unfortunately, the authors did not provide any more information from their statistical analysis to clarify the strength of the relationship.

Again, there was a significant relationship of SDQ score with tap water fluoride concentration but in this case, it was only significant for older youth and the CI was also relatively large.

So again the relationships with tap water F are tenuous – influenced by age and with large confidence intervals indicating a wide scatter in the data.

Problems with the paper’s discussion

Of course, correlation by no means implies causation. But there is always the problem of confirmation bias and special pleading where a low p-value in a regression analysis gets construed as evidence for the preferred outcome.

There are problems with relying only on p-values – which is why I have referred to confidence intervals and would prefer to actually see the actual data and full reports of the statistical analyses. The confidence interval values indicate that the data is highly scattered and the reported models from the regression analyses in this paper probably explain very little of the data. In such cases, there is a temptation to dig deeper and search for significant relationships by separating the data by sex or age but the resulting significant relationships may be meaningless.

And the “Elephant in the Room” – the relationships themselves say nothing about the reliability of the favored model. Nothing at all. A truly objective researcher would recognize this and avoid the staw clutching and rationalisation of evidence in the paper’s discussion. For example, the author’s considered another Canadian study which did not find any relationship of ADHD to fluoride in drinking water and argued the difference was solely due to deficiencies in the other study, not theirs.

The authors also seem not to recognise that any relationship they found may have nothing to do with fluoride but could be the result of other related risk-modifying factors they did not include in their statistical analysis. Worse, the argue their results are consistent with those of Malin and Till (2015) without any acknowledgment that that specific study is flawed. Perrott (2108) showed that the relationship reported by Malin & Till (2015) disappeared completely when the altitude was included in the statistical analysis. This is consistent with the study of Huber et al., (2015) which reported a statistically significant relationship of ADHD prevalence with altitude.

Conclusion

I think the Riddell et al., (2109) paper presents problems similar to those seen with a previous paper from this research group – Green et al., (2019). I have discussed some of these problems in previous articles:

Others in the scientific community have also expressed concern about the problems in that paper and a recent in-depth critical evaluation of (see CADTH RAPID RESPONSE REPORT: Community Water Fluoridation Exposure: A Review of Neurological and Cognitive Effects) pointed to multiple “limitations (e.g., non-homogeneous distribution of data, potential errors and biases in the estimation of maternal fluoride exposure and in IQ measurement, uncontrolled potential important confounding factors).” It urged that “the findings of this study should be interpreted carefully.”

More significantly widespread scientific concern about weaknesses in the Green et al., (2019) paper has led  30 scientific and health experts to write to the funding body involved (US National Insitute of Environmental Health Science – NIEHS) outlining their concern and appealing for the data to be made public for independent assessment (see Experts complain to funding body about quality of fluoride-IQ research Download their letter). Last I was aware the authors were refusing to release their data – claiming not to own it!

We could well see similar responses to the Riddell et al., (2109) ADHD paper.

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Experts complain to funding body about quality of fluoride-IQ research

Science should never be protected from critical and rational discussion. Funding bodies should also be aware of problems in the research they fund. Image credit: The value of experience in criticizing research.

The scientific community was generally critical of the recent Canadian maternal neonatal fluoride – child IQ research (see expert reaction to study looking at maternal exposure to fluoride and IQ in children). But this has now taken a more serious turn.  Thirty academics and professional experts from health and dental institutions in the US, Canada, UK, Ireland, and Australia have formally complained to the US National Insitute of Environmental Health Science (NEHS) about the study.

This is highly important as the NEHS is the funding body for this research. If it takes seriously the criticisms of poor quality of the research and its bias it could well mean these study authors lose their funding.

I have covered professional criticism of this study in previous articles (and included some of my own critical comments). See:

Here is the letter to the NIEHS – readers can download and read it for themselves. I urge you to do this as there may well be a lot of misrepresentation circulating in the near future if anti-fluoride activists launch a campaign to discredit it.

Release of data and methodology requested

The letter requests the NIEHS:

“formally ask the Green authors to release the HIPAA-compliant, Research Identifiable File (RIF) data sets from their study, as well as a complete explanation of their methods and the computer program/codes used in their data management and analysis.”

This request is motivated by the fact that several of the study authors “have declined to respond affirmatively to requests from other researchers for access to the data and analytical methods they used.”

I know that study authors have gone even further – for example, asking that a university department pressure one of their research students to remove social media discussion of the study. Unfortunately, the student did remove his posts – but I can understand the power of institutional pressure.

I think such to such limiting of critical post-publication discussion is ethically unscientific as it inhibits true peer review. It’s made worse in this situation as the journal has a policy of restricting publishing any critiques of papers to four weeks after publication. The journal editor did refer to “the implications of this study” being “debated in the public arena” – but it appears that the authors are not exactly keen on that either.

Large range of problems with the Canadian study

The letter lists a number of problems with the Canadian study. These include:

  1. Focusing on a subgroup analysis amid “noisy data”:
  2. Modeling and variable anomalies:
  3. Lacking data on relevant factors:
  4. Omitting crucial findings:
  5. Using invalid measures to determine individual exposures:
  6. Defining the final study group:
  7. Assessing the impact of fluoride exposure:
  8. Reporting anomalies:
  9. Internal inconsistency of outcomes:
  10. Overlooking research that conflicts with the authors’ conclusions:

I urge readers who are interested in either of these aspects to refer to the letter for details of the problems. The letter includes a list of 30 references relevant to these problems and to criticisms of the study by other professionals.

Scientific politics

In Politics of science – making a silk purse out of a sow’s ear I raised the problems presented by scientific politics where poor studies are often promoted by journals, institutions, and authors. Maybe that is to be expected – science is a human activity and therefore subject to human problems like ambition and self-promotion.

Billboards like this misrepresent the Canadian research. But self-promotion and ambition of researchers and authors provide “authoritative” statements that activists use for such fake advertising.

However, in this case, scientific ambition and self-promotion have led to apparently “authoritative” statements by professionals that have been used to feed the scaremongering of anti-fluoride activists. These professionals may argue they are careful to qualify their statements but in the end, they must bear a lot of responsibility for the sort of completely misleading and false advertising activists have been promoting. Advertising which has serious consequences because of its scaremongering.

Scaremongering and scientific integrity

The letter also raises the problem of scaremongering in its final paragraph:

“. . . the Green article could generate unjustified fear that undermines evidence-based clinical and public health practices. So much is at stake. Hundreds of millions of people around the globe—from Brazil to Australia—live in homes that receive fluoridated drinking water. Hundreds of millions of people use toothpaste or other products with fluoride. Many millions of children receive topical fluoride treatments in clinical or other settings. Tooth decay remains one of the most common chronic diseases for children and teens, and fluoride is a crucial weapon against this disease. Decay prevalence could increase if a journal article unnecessarily frightens people to avoid water, toothpaste or other products fortified with fluoride.”

This letter by 30 high ranking professionals is extremely important. The concerns it raises are very relevant to scientific integrity and hence scientific credibility. I hope that the NIEHS and similar bodies will take on board the responsibility they have to ensure the work they fund is credible, expert, scientifically authentic and as free as possible from personal scientific ambitions and biases.

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What do these mother-child studies really say about fluoridation?

A list of indicators of bad science – many of these are found in articles promoted by anti-fluoride activists.

Anti-fluoride activists have been pouring money into a scaremongering campaign warning pregnant women not to drink fluoridated water. They claim fluoride will lower the IQ of their future child.

Fluoride Free NZ (FFNZ)  launched this campaign on the back of the recent publication of Canadian research on fluoride intake by pregnant women and child IQ (see Ground Breaking New Study – Top Medical Journal – Fluoridated Water Lowers Kids’ IQs). Now they are responding to criticisms of this paper by claiming it is supported by other research and claim a list of 6 papers support their claim that pregnant women drinking fluoridated water may be harming the IQ of their children.

Image used by FFNZ as part of their appeal for finance to support billboards and newspaper advertising promoting their false scaremongering claim.

None of these studies actually support the FFNZ claim. Let’s consider each of the six studies listed by the Fluoride Action Network (FAN) – but first, look at this data from the “New Study – Top Journal” mentioned by FFNZ:

This is from Table 1 in  Green et al., (2019). There are no statistically significant differences in the IQ o children whose mothers lived in either fluoridated or nonfluoridated areas during their pregnancy. So the FFNZ claim is completely false.

The Fluoride Action Network (FAN) Bulletin Several In Utero Fluoride/IQ Studies Should Provoke A Nation-Wide Fluoridation Moratorium initially list 5 studies but expanded this to 6 studies in an article posted by Ellen Connett the same day – The 6 Mother-Offspring Fluoride Studies. Here is the list (descriptions are from the FAN articles):

  • Green et al.,  (2019). Largest study with 512 mother-offspring. Lower IQ in children 3- 4 years of age.
  • Bashash et al., (2017). Longest study. 299 mother-offspring pairs in Mexico. Lower IQ in children 4 and 6-12 years of age.
  • Thomas et al., (2018). 401 mother-offspring pairs in Mexico. Lower IQ in children 1-3 years of age Only the abstract has been published.
  • Valdez Jiménez (2017). Lower IQ between the ages of 3-15 months with 65 mother-offspring pairs in Mexico.
  • Li et al., (2004). Significant differences in the neonatal behavioral neurological assessment score in 91 offspring aged 1-3 days old.
  • Chang et al., (2017). Reported significant differences in the mental development index and psychomotor development index of the offspring at 3, 6, 9, and 12 months of age.

I will consider these in three groups and include some relevant theses.

The Chinese studies from areas of endemic fluorosis

These describe data from areas of endemic fluorosis in China. They are irrelevant to community water fluoridation as the drinking water fluoride concentrations are much higher and people in these areas suffer a range of health problems including severe skeletal and dental fluorosis. Remember, the optimum levels of fluoride recommended for community water fluoridation are generally below 1 mg/L and WHO recommends drinking water concentrations should be lower than 1.5 mg/L.

People in areas of endemic fluorosis suffer a range of health problems

Li, J., Yao, L., Shao, Q. L., & Wu, C. Y. (2008). Effects of high fluoride level on neonatal neurobehavioral development. Fluoride, 41(2), 165–170.

This is one of the generally poor quality studies FAN got translated to assist their campaign. They are generally published in Fluoride, an anti-fluoride journal.

It compared children from villages with drinking water concentrations of  1.7–6.0 mg/L with a control group from villages with drinking water concentrations of 0.5–1.0 mg/L.

Chang A, Shi Y, Sun H, Zhang L. (2017) Analysis on the Effect of Coal-Burning Fluorosis on the Physical Development and Intelligence Development of Newborns Delivered by Pregnant Women with Coal-Burning Fluorosis.

Another one of the poor quality papers  FAN got translated but they have made only the abstract and a brief abstract available on their site. I cannot find a full test or even abstract anywhere else. It compares two groups:

“A total of 68 newborns delivered by pregnant women from coal-burning endemic fluorosis areas in this region were selected as an observation group, and 50 full-term newborns delivered by normal healthy pregnant women were selected as a control group. “

Both papers report statistically significant differences in some measurements between the two groups but that is to be expected for areas of endemic fluorosis and especially where coal-burning creates health problems. Of course, this is all irrelevant to community water fluoridation as only the control groups are likely to be drinking water with similar fluoride concentrations.

Mexican study from an area of endemic fluorosis

The paper is:

Valdez Jiménez, L., López Guzmán, O. D., Cervantes Flores, M., Costilla-Salazar, R., Calderón Hernández, J., Alcaraz Contreras, Y., & Rocha-Amador, D. O. (2017). In utero exposure to fluoride and cognitive development delay in infants . Neurotoxicology

This study found a relationship of child cognitive deficits with maternal prenatal urinary fluoride but, again, it is not relevant to community water fluoridation. The authors acknowledge that the study was done in an area of endemic fluorosis.  About 90% of the drinking water samples in the study contained fluoride above the World Health recommended maximum of 1.5 mg/l and the Fluoride in the mothers’ urine was also high – with the mean concentration for all the mothers of 1.9 mg/l  for the 1st trimester, 2.0 mg/l for the 2nd and 2.7 mg/l for the 3rd trimester. Urinary fluoride concentrations as high as 8.2 mg/l were found. This compares with a mean value for urinary F of 0.65 mg/L for pregnant women residents in areas with low levels of F in drinking water (0.4 to 0.8 mg/l – similar to that recommended in community water fluoridation).

I have written about this study in my post Premature births a factor in cognitive deficits observed in areas of endemic fluorosis? There I speculated that the effect of fluoride on cognitive deficits may be indirect because of the observede higher incidence of prematurity and low birth rate.

The Bashash study

I have separated these from the Green et al., (2019) study although they both report relationships between maternal prenatal urinary fluoride and the IQ of offspring and many of the authors are common to both studies  Bashash is the senior author on the paper reporting data from Mexico city and Green on the paper reporting data for Canada.

There are really three citations for this study. The main paper:

Bashash, M., Thomas, D., Hu, H., Martinez-Mier, E. A., Sanchez, B. N., Basu, N., … Hernández-Avila, M. (2017). Prenatal fluoride exposure and cognitive outcomes in children at 4 and 6–12 years of age in Mexico. Environmental Health Perspectives, 125(9), 8–10.

A conference poster:

Thomas, D., Sanchez, B., Peterson, K., Basu, N., Angeles Martinez-Mier, E., Mercado-Garcia, A., … Tellez-Rojo, M. M. (2018). OP V – 2 Prenatal fluoride exposure and neurobehavior among children 1–3 years of age in Mexico. Environmental Contaminants and Children’s Health, 75(Suppl 1), A10.1-A10. https://doi.org/10.1136/oemed-2018-ISEEabstracts.23

And Deena Thomas’s Ph. D. thesis which also reported data from the study:

Thomas, D. B. (2014). Fluoride exposure during pregnancy and its effects on childhood neurobehavior: a study among mother-child pairs from Mexico City, Mexico. University of Michigan.

The anti-fluoride activists have waxed lyrical about the reported negative relationship of child IQ with maternal prenatal urinary F concentrations but they are clutching at statistical straws as, in fact, the relationship is very weak – explaining only a few percent of the IQ variance. I explained this in my post Fluoride, pregnancy and the IQ of offspring, and described several other problems (correlation is not evidence of causation, information about the mothers is scant with no indication if they lived in areas of endemic fluorosis, possible important risk-modifying factors were not considered as confounders, urinary fluoride is not a good indicator of fluoride intake by individuals, and there was no association of child IQ to child urinary fluoride).

When data has this much scatter the marginal statistical significance of relationship are easily altered by tweaking the data. (Fig 3a from Bashash et al., 2017).

It is easy to be misled by marginal statistically significant relationships when considering data with such a high scatter. This is illustrated by the conclusions of one of the authors, Deena Thomas, in her Ph. D. thesis that:

“Neither maternal urinary or plasma fluoride was associated with offspring MDI scores” [page 37);

“This analysis suggests that maternal intake of fluoride during pregnancy does not have a strong impact on offspring cognitive development in the first three years of life.” [page 38];

“Maternal intake of fluoride during pregnancy does not have any measurable effects on cognition in early life.” [page 48].

Yet, in the conference poster  based on her thesis she concluded:

“Our findings add to our team’s recently published report on prenatal fluoride and cognition at ages 4 and 6–12 years by suggesting that higher in utero exposure to F has an adverse impact on offspring cognitive development that can be detected earlier, in the first three years of life.”

Her conclusions reported in her thesis are exactly the opposite of the conclusions reported in her conference poster!

I suggest in my article A conference paper on the maternal prenatal urinary fluoride/child IQ study has problems that the different conclusions in the poster resulted from the fact that at least 30 mother-child pairs were removed from the data set used in her thesis (the thesis consider 431 mother-child pairs but the poster considered only 401 pairs). Perhaps even some data pairs were added (the maximum urinary F value is higher in the smaller data set used for the poster).

In her thesis, Deena Thomas also reported: “concurrent urinary fluoride exposure has a strong positive impact on cognitive development among males aged 6-15 years.” [page 54]. The relationship was not significant for females. But the actual paper, Bashash et al., (2017), concluded “there was not a
clear, statistically significant association between contemporaneous children’s urinary fluoride . . . and IQ. “

I have discussed the Mexican maternal prenatal urinary fluoride- IQ study in more detail in the following articles:

A draft of my article critiquing the Bashash et al., (2017) paper, “Predictive accuracy of a model for child IQ based on maternal prenatal urinary fluoride concentration” is also available online. I have also discussed another paper from this study (Bashash et al., 2018) which reported a weak relationship of ADHD prevalence with maternal urinary fluoride in my article Fluoridation and ADHD: A new round of statistical straw clutching.

Green et al., paper/thesis

This study is actually the only one that included people exposed to community water fluoridation – hence the relevance of the data I presented in the introductory table which showed no effect. But the study it is basically the same as that of Bashash et al (2017) except it involved Canadian mother-child pairs and most of the criticism of Bashash et al., (2018)  are relevant to the Green et al., (2019) study which has been reported in the following forms:

Green, R., Lanphear, B., Hornung, R., Flora, D., Martinez-Mier, E. A., Neufeld, R., … Till, C. (2019). Association Between Maternal Fluoride Exposure During Pregnancy and IQ Scores in Offspring in Canada. JAMA Pediatrics, 1–9.

Green, R. (2018). Prenatal Fluoride Exposure and Neurodevelopmental Outcomes in a National Birth Cohort (MSc thesis, Graduate Program in Psychology York University Toronto, Ontario). 

My original critique included a conclusion that the reported negative relationship of child IQ with maternal prenatal urinary F concentration was extremely weak. I found that it explained only 1.3% of the child IQ variance using data extracted from the figures. Subsequently Rivka Green claimed an R-squared value of 4.7% which is still very low (we can reject her claim that it was “quite high” as simple promotion of her work).

For further discussion of the Green et al (2019) study see my articles:

A problem of self-promotion and confirmation bias

Science and the scientific literature are, of course, not immune to self-promotion and confirmation bais and I think the maternal urinary fluoride-child IQ studies show this. I discussed this in If at first you don’t succeed . . . statistical manipulation might help as well as pointing out that these scientific politics are amplified by activist propaganda.

This is a pity because such confirmation bias and self-promotion may result in important information being overlooked. I discussed this in  my article A more convincing take on prenatal maternal dietary effects on child IQ which referred to another paper from the Mexican maternal urinary fluoride study which showed a relationship of child IQ with maternal nutrition:

Malin, A. J., Busgang, S. A., Cantoral, A. J., Svensson, K., Orjuela, M. A., Pantic, I., … Gennings, C. (2018). Quality of Prenatal and Childhood Diet Predicts Neurodevelopmental Outcomes among Children in Mexico City. Nutrients, 10(8), 1093.

Again the relationships reported were weak, but the negative relationship of child IQ with poor prenatal maternal nutrition explains 11.2% of the variance in child IQ – much better than the data for prenatal maternal urinary fluoride (which explained only 3% of the variance).

Conclusions

So what do these mother-child studies say about community water fluoridation and IQ?

Well, nothing really – except that the only study which compared fluoridated and non-fluoridated areas showed absolutely no effect.

But this will not stop activists (and unfortunately self-promoting scientists and their institutions) from making unwarranted claims. Their propaganda relies on unsupported “authority” opinion and misrepresentation. This violates many of the rules in my first image above.

It tries to present correlation as proof of causation, misinterprets results, promotes unsupported conclusions, selectively reports the data and findings, and expands these unsupported conclusions way beyond the small sample sizes used.

This is just bad science!

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Biostatistical problems with the Canadian fluoride/IQ study

There are insights in there somewhere. Image Credit: DATA ANALYTICS COMES TO THE LEGAL PROFESSION

There has been widespread scientific criticism of the recently published Canadian fluoride-IQ study of Green et al., (2019). Most recently Dr. René F. Najera (a Doctor of Public Health, an epidemiologist and biostatistician) has critiqued the statistical analysis. He finds a number of faults and concludes by hoping “public health policy is not done based on this paper:”

 “It would be a terrible way to do public health policy. Scientific discovery and established scientific facts are reproducible and verifiable, and they are based on better study designs and stronger statistical outcomes than this. “

Dr. Najera’s critiques the biostatistics is in his article The Hijacking of Fluorine 18.998, Part Three. This follows his previous critique (Part 1 and Part 2) of the epidemiological issues which I reviewed in Fluoridation – A new fight against scientific misinformation.

Dr. Najera starts by stressing the important role of biostatistics in epidemiological studies. After all the planning and measurement:

“.. we hand off the data to biostatisticians, or we do the work with biostatisticians. Doing this assures us that we are measuring our variables correctly and that all associations we see are not due to chance. Or, if chance had something to do with it, we recognize it and minimize the factors that lead to chance being a factor in and of itself.”

I agree completely. In my experience statisticians play a critical role in research and should be involved even at the planning stage. Further, I think the involvement of experienced biostatisticians is invaluable. Too often I see papers where the authors themselves relied on their own naive statistical analyses rather than calling on experience. Perhaps they are being protective of their own confirmation bias.

The specific study Dr. Najera critiques is:

Green, R., Lanphear, B., Hornung, R., Flora, D., Martinez-Mier, E. A., Neufeld, R., … Till, C. (2019). Association Between Maternal Fluoride Exposure During Pregnancy and IQ Scores in Offspring in Canada. JAMA Pediatrics, 1–9.

For my other comments on the Candian fluoride/IQ research see:

No comparison group

One problem with this study is that a number of mother-child pairs were excluded and, in the end, the sample used was not representative of the Canadian population. Najera summarises the “main finding of the study as “that children of mothers who ingested fluoride during pregnancy had 4 IQ points lower for each 1 mg of fluoride consumed by the mother:”

“If you’re asking yourself, “Compared to whom?” you are on the right track. There was no comparison group. Women who did not consume tap water or lived outside a water treatment zone were not included, and that’s something I discussed in the previous post. What the authors did was a linear regression based on the data, and not much more.”

In fact, while the sample used was unrepresentative the study did compare the IQs of children whose mothers had lived in fluoridated and nonfluoridated areas. There was no statistically significant difference – an important fact which was not discussed at all in the paper. This table was extracted from the paper’s Table 1.

What about that regression?

While ignoring the mean values for fluoridated and nonfluoridated areas the authors relied on regression analyses to determine an effect.

But if you look at the data  in their Figure 3A reproduced below you can see problems:

“. . . you can see that the average IQ of a child for a mother consuming 1.5 mg of fluoride is about 100. You also see that only ONE point is representing that average. That in itself is a huge problem because the sample size is small, and these individual measurements are influencing the model a lot, specially if their value is extreme. Because we’re dealing with averages, any extreme values will have a disproportionate influence on the average value.”

Several scientific commenters on this paper have noted this problem which is important because it should have been dealt with in the statistical analysis:

“When biostatisticians see these extreme values popping up, we start to think that the sample is not what you would call “normally distributed.” If that is the case, then a linear regression is not exactly what we want to do. We want to do other statistical analyses and present them along with the linear regressions so that we can account for a sample that has a large proportion of extreme values influencing the average. Is that the case with the Green study? I don’t know. I don’t have access to the full dataset. But you can see that there are some extreme values for fluoride consumption and IQ. A child had an IQ of 150, for example. And a mother consumed about 2.5 milligrams of fluoride per liter of beverage. Municipal water systems aim for 0.7 mg per liter in drinking water, making this 2.5 mg/L really high.”

No one suggests such outliers be removed from the analyses (although the authors did remove some). But they “should be looked at closely, through statistical analysis that is not just a linear regression.”

This is frustrating because while the authors did not do this they hint that it was considered (but do not produce results)  when they say:

“Residuals from each model had approximately normal distributions, and their Q-Q plots revealed no extreme outliers. Plots of residuals against fitted values did not suggest any assumption violations and there were no substantial influential observations as measured by Cook distance. Including quadratic or natural-log effects of MUFSG or fluoride intake did not significantly improve the regression models. Thus, we present the more easily interpreted estimates from linear regression models.”

As Dr. Najera comments, this is “.. worrisome because that is all they presented. They didn’t present the results from other models or from their sensitivity analysis.”

Scientific commenters are beginning to demand that the authors make the data available so they can check for themselves. My own testing with the data I extracted from the figure does show that the data is not normally distributed. Transformation produced a normal distribution of the data but the relationship was far weaker than for a straight linear regression. Did the authors reject transformations simply because they  “did not significantly improve the regression models?”

That suggests confirmation bias to me.

Confidence intervals

In their public promotions, the authors and their supporters never mention confidence intervals (CIs)- perhaps because the story does not look so good when they are considered. Most of the media coverage has also ignored these CIs.

A big thing is made for the IQ score of boys dropping by 4.49 points with a 1 mg/L increase in  mother’s urinary fluoride, but:

“Based on this sample, the researchers are 95% confident that the true drop in IQ in the population they’re studying is between 0.6 points and 8.38 points. (That’s what the 95% CI, confidence interval, means.)”

In other words:

“In boys, the change is as tiny as 0.6 and as huge as 8.38 IQ points.”

For girls the change:

“is between -2.51 (a decrease) and 7.36 (an increase). It is because of that last 95% CI that they say that fluoride ingestion is not associated with a drop in IQ in girls. In fact, they can’t even say it’s associated with an increase. It might even be a 0 IQ change in girls.”

Dr. Najera asks:

“Is this conclusive? In my opinion, no. It is not conclusive because that is a huge range for both boys and girls, and the range for girls overlaps 0, meaning that there is a ton of statistical uncertainty here. “

This is why the epidemiological design used by the authors is worrying. For example:

” The whole thing about not including women who did not drink tap water is troubling since we know that certain drinks have higher concentrations of fluoride in them. If they didn’t drink tap water, what are the odds that they drank those higher-fluoride drinks, and what was the effect of that?”

This comes on top of the problems with the regression models used.

Transformation to normalise the data and inclusion of other important facts may have produced a non-significant relationship and there would be no need for this discussion and speculation.

What about those other important factors?

Green et al (2019) included other factors (besides maternal urinary fluoride) in their statistical model. This “adjustment” helps check that the main factor under consideration is still statistically significant when other factors are included. In this case, the coefficient (and CIs) for the linear association for boys was reduced from -5.01  (-9.06 to -0.97) for fluoride alone to -4.49 (-8.38 to -0.60) when other considered factors were included. In this case, the other factors included race/ethnicity, maternal education, “city”, and HOME score (quality of home environment).

Dr. Najera questions the way other factors, or covariates, were selected for inclusion in the final model. He says:

“The authors also did something that is very interesting. They left covariates (the “other” factors) in their model if their p-value was 0.20. A p-value tells you the probability that the results you are observing are by chance. In this case, they allowed variables to stay in their mathematical model if the model said that there was as much as a 1 in 5 chance that the association being seen is due to chance alone. The usual p-value for taking out variables is 0.05, and even that might be a little too liberal.

Not only that, but the more variables you have in your model, the more you mess with the overall p-value of your entire model because you’re going to find a statistically significant association (p-value less than 0.05) if you throw enough variables in there. Could this be a case of P Hacking, where researchers allow more variables into the model to get that desired statistical significance? I hope not.”

Good point. I myself was surprised at the use of such a large p-value for selection. And, although the study treats fluoride as the main factor and inclusion of the other factors reduces the linear coefficient for maternal urinary fluoride, I do wonder why more emphasis was not put on these other factors which may contribute more to the IQ effect than does fluoride.

Perhaps this paper should have concentrated on the relationship of child IQ with race or maternal education rather than with fluoride.

Padding out to overcome the poor explanation of IQ variance

Another point about the inclusion of these covariates. As well as possible improving the statistical significance of the final model they may also make the model look better in terms of the ability to explain the variance in IQ (which is very large – see figure above).

In my first critique of the Green et al (2019) paper (If at first you don’t succeed . . . statistical manipulation might help) I pointed out that the reported relationship for boys, although statistically significant, explained very little of the variance in IQ. I found only 1.3% of the variance was explained – using data I had digitally extracted from the figure. This was based on the R-squared value for the linear regression analysis.

Unfortunately, the authors did not provide information like R-squared values for their regression analysis (poor peer review in my opinion) – that is why I, and others, were forced to extract what data we could from the figures and estimate our own. Later I obtained more information from  Green’s MA thesis describing this work (Prenatal Fluoride Exposure and Neurodevelopmental Outcomes in a National Birth Cohort). Here she reported an R-squared value of 4.7%. Bigger than my 1.3% (my analysis suffered from not having all the data) but still very small. According to Nau’s (2017) discussion of the meaning of R-squared values (What’s a good value for R-squared?), ignoring the coefficient determined by Green et al (2019) (5.01) and relying only on the constant in the relationship would produce a predicted value of IQ almost as good (out by only about 2%).

That is, simply taking the mean IQ value (about 114.1 according to the figure above) for the data would be almost as good as using the relationship for any reasonable maternal urinary fluoride value and OK for practical purposes.

But look at the effect of including other factors in the model. Despite lowering the coefficient of the relationship for fluoide it drastically increases the R-squared value. Green reported a value of 22.0% for her final model. Still not great but a hell of a lot better than 4.7%.

Perhaps the inclusion of so many other factors in a multiple regression makes the final model look much better – and perhaps that perception is unjustly transferred to the relationship with fluoride.

Are other more important factors missed?

Almost certainly – and that could drastically alter to conclusions we draw from this data. The problem is that fluoride can act as a proxy for other factors. City location and size are just one aspect to consider.

In my paper Fluoridation and attention deficit hyperactivity disorder a critique of Malin and Till (2015), I showed inclusion of altitude as a risk-modifying factor completely removed any statistical significance from the relationship between ADHD prevalence and fluoridation – despite the fact Malin & Till (2015) had reported a significant relationship with R-squared values over 30%!

Malin & Till (2015) reported these relationships as statistically significant. However, when altitude was included in the multiple regressions by Perrott (2018) no significant relationships were fluoridation were found.

So you can see the problem. Even though authors may list a number of factors or covariates they “adjusted” for, important risk-modifying factors may well be ignored in such studies. This is not to say that inclusion of them “proves” causation any more than it does for fluoride. But if their inclusion leads to the disappearance of the relationship with fluoride one should no longer claim there is one (reviewers related to the group involved in the Green et al., 2019 study still cite Malin & Till 2015 as if their reported relationship is still valid).

In effect, the authors acknowledge this with their statement:

“Nonetheless, despite our comprehensive array of covariates included, this observational study design could not address the possibility of other unmeasured residual confounding.”

Summary

Dr. Najera summarises his impression of the Green et al (2019) study in these words:

“The big idea of these three blog posts was to point out to you that this study is just the latest study that tries very hard to tie a bad outcome (lower IQ) to fluoride, but it really failed to make that case from the epidemiological and biostatistical approaches that the researcher took, at least in my opinion. Groups were left out that shouldn’t. Outliers were left in without understanding them better. A child with IQ of 150 was left in, along with one mother-child pair of a below-normal IQ and very high fluoride, pulling the averages in their respective directions. The statistical approach was a linear regression that lumped in all of the variables instead of accounting for different levels of those variables in the study group. (A multi-level analysis that allowed for the understanding of the effects of society and environment along with the individual factors would have been great. The lack of normality in the distribution of outcome and exposure variables hint at a different analysis, too.)”

Pretty damning!

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Fluoridation – A new fight against scientific misinformation

Anti-fluoride campaigners think a new Canadian fluoride IQ study is the best thing since sliced bread but the scientific critiques warn they are wrong. Photo Illustration by The Daily Beast/Getty

The new Canadian fluoride-IQ study has certainly created some sensational reporting. On the one hand, anti-fluoride campaigners are lauding the study as the best things since sliced bread and seem sure it will lead to the end of community water fluoridation. Mainstream media have featured the findings – although in most cases warn they are controversial and may be meaningless. As would be expected, alternative health media have been promoting it and repeating the anti-fluoridation arguments.

However, scientific commenters have mainly criticised the study and warned that even if the findings are valid it is just one study and it is far too early to consider stopping community water fluoridation – a health policy which is so far been seen as economical, safe and effective in helping fight tooth decay.

I strongly believe the scientific critiques are important. One should not rely on “authority” statements in such matters – especially statements from well known anti-fluoride activists. But we should also be aware that self-promotion by the authors and journal, and by the authors’ institutions, is also not a reliable indicator of the worth of a study.

In the end, the validity and worth of this study will depend on the data and methodology – and good scientific critiques will look at these, not the status of the journal, institutions or authors. And not the public statements being made to promote the findings.

Some interesting critiques are coming from Dr. René F. Najera who is a Doctor of Public Health, an epidemiologist and biostatistician. These are the very skills essential for a proper critique of the Canadian study.

The specific study Dr. Najera refers to is:

Green, R., Lanphear, B., Hornung, R., Flora, D., Martinez-Mier, E. A., Neufeld, R., … Till, C. (2019). Association Between Maternal Fluoride Exposure During Pregnancy and IQ Scores in Offspring in Canada. JAMA Pediatrics, 1–9.

For my other comments on the Candian fluoride/IQ research see:

The “shenanigans” of activists

In his first article, The Hijacking of Fluorine 18.998, Part One, Dr. Najera gives some background. He says:

“Time after time, epidemiological studies have shown that fluoridated water leads to less tooth decay. Less tooth decay leads to better health outcomes as poor oral health is a risk factor for a variety of conditions. At the same time, all of these studies failed to see any association between bad outcomes and fluoridation done correctly.”

And

” . . those people who were suspicious of putting fluoride in the water did what people who are suspicious of public health interventions often do: they heard of some bad outcome of ingesting fluoride (which is a compound made up of fluorine, the chemical element), amplified it, exaggerated it and showed it as the ultimate example of what fluoride consumption at any concentration can do to a person.”

He compares this to “the shenanigans of the anti-vaccine crowd” and concludes that:

“…just like we had to do in the late 1990s with the Wakefield Fraud “study” that was not a study, here we go fighting a new fight against misinformation…”

He concludes this because:

“In consultation with friends and colleagues, we found a lot to be worried about in the epidemiological design of the study and the biostatistical analysis of the resulting data… And, of course, of the conclusions reached by the authors and the press (with some help from the authors). “

Some epidemiological concerns

In his second article, The Hijacking of Fluorine 18.998, Part Two, Dr. Najera expresses his epidemiological concerns about the research. These include:

1: Unwarranted exclusion of some mother-child pairs:

“For example, some were excluded because they did not drink tap water or lived outside a water treatment zone. Wouldn’t you want to know if not drinking tap water or living outside a water treatment zone led to children with normal-to-high IQs compared to the others?

This raised flags with me because I don’t exclude someone from an outbreak investigation if they don’t have a desired exposure. In fact, I want to know if someone who is not exposed to something is less likely to develop the disease or have the condition I’m studying. It would be like saying that I don’t want women who live in air-conditioned apartments in a city included in a study on Zika because they are not likely to have been exposed to mosquitoes like women living in huts in the jungle.”

2: Overlap of groups:

“In the end, they had 369 mother-child pairs with mean urine fluoride (MUF) measurements, IQ measurements and water fluoride data and 400 mother-child pairs with fluoride intake and IQ measurements. But that’s 769 pairs when 610 children were originally considered? Yes, there is some overlap between the two groups. No big deal if they do their biostats right. (Spoiler alert for Part Three: They didn’t.)”

3: Urinary fluoride data questionable:

“They then used data on mean urine fluoride concentrations from spot (one-time) urine samples taken at different points in the mothers’ pregnancies, and they only accepted those who had been tested throughout (i.e. didn’t miss a test). The problem with this is that the standard to really know how much fluoride someone is exposed to — by testing their urine — is a 24-hour collection of urine. In that test, you have someone collect their urine for 24 hours and then we measure the fluoride (or a lot of other chemicals) in that sample. This is because urine concentrations of chemicals vary throughout the day. If you drink a lot of fluoridated water in the morning, then your urine is likely to have higher concentrations shortly thereafter than in the evening, when you’ve been drinking bottled water without fluoride. Or, if you worked out in the morning and drank energy drinks but stuck to only tap water in the evening, your urine fluoride will be different.”

Other scientific commenters have also been critical of the urinary fluoride data.  Dr F. Perry Wilson suggests that blood plasm fluoride would have been a far better indicator of fluoride intake (see More expert comments on the Canadian fluoride-IQ paper).

The World Health Organisation’s (WHO) recommendations on the monitoring total fluoride intake for populations also stress the need for 24-hour collection and warn that “urinary fluoride excretion is not suitable for predicting fluoride intake for individuals.” [WHO’s emphasis] (see Anti-fluoridation campaigner, Stan Litras, misrepresents WHO).

WHO recommends it only for monitoring fluoride intake of groups of people because of the large effects of individual diets (see Basic Methods for Assessment of Renal Fluoride Excretion in Community Prevention Programmes for Oral Health). But in this Canadian study, urinary fluoride values were used to estimate individual intake of fluoride.

4: Fluoride intake assessed via an unvalidated survey:

“This means that it is hard to know if the survey really measures what it is supposed to measure. Still, they used it, and it leaves the study wide open to recall bias, something you want to minimize as much as possible. And they would have minimized it if they used it a more valid survey, or a prospective design to their study.

First, what is a prospective design? Well, this is when you take a group of women and sign them up for the study, then you carefully measure their fluoride intake with more validated laboratory assays and questionnaires, and then you follow their children and measure their IQ periodically. You don’t do it all retrospectively with already collected data. But, sometimes, what you have is what you have.

Next, what is recall bias? Recall bias is this interesting phenomenon we see when we rely on people telling us their story in order to ascertain risks and outcomes of exposures. We epidemiologists have noticed that people who have bad outcomes tend to be more likely to remember significant exposures. For example, parents of children with birth defects are more likely to remember things like exposures to chemicals or a history of disease in the family. While parents of typical children don’t recall similar exposures as much because, well, they aren’t looking to connect any dots.

(You see this all the time in anti-vaccine circles, where parents of autistic children are more likely to recall bad reactions to vaccines in their children.)”

Dr. Najera also finds this methodology strange because “they multiplied the intake of certain drinks by some factors in order to estimate fluoride intake:”

“This complicates things because, as you saw above, they excluded women who were not in places where the water was being treated and women who didn’t consume tap water. But, come on, have you ever met someone who never consumed tap water? Do we not use tap water to cook foods all the time? What about that fluoride intake? And why just multiply for fluoride in beverages and not, say, that delicious Canadian cheese soup I’ve heard good things about?”

5: Problems with IQ testing of children:

“I’ve asked some friends of mine who are experts in childhood development, and they are skeptical of accurate measurements of IQ in children because children develop at different rates depending on a variety of variables. You may have seen this when you look at a classroom or a school play. Children are on a big spectrum of development, with milestones being really more like average moments.”

6: Sample not representative:

“The sample used in this study is not at all representative of all mothers and their children in Canada, not even close. As we saw in the paper, many women were left out of the study for a variety of reasons, and mother-child pairs were also excluded. I want to believe that there were good reasons for this, but I could not find them in the paper. The authors do mention that they wanted to look only at mothers consuming fluoride, but why not include those who were not expected or outright did not consume fluoride in order to really compare two populations of interest?”

Dr. Najera finishes with a general comment about the way other studies in the scientific literature are used to provide credibility to the findings;

“Finally, the authors mention other studies — some with rats, other purely environmental — where there is some association between fluoride intake and lowered IQ or some sort of negative impact to neurodevelopmental delay. The thing is, public health agencies around the whole world have been looking at these claims and not finding them to be true within their populations. “

I also find the practice concerning, especially as it is relatively common. I think it indicates confirmation bias – the authors making citations that they think support their findings (and purposely refraining from citing studies that don’t). I find this practice disingenuous because it never qualifies the citations with any reference to the applicability to the real-life situation of community water fluoridation. It never points out the high fluoride concentrations used in animal studies or the fact that many research articles on fluoride and child IQ have involved populations in areas of endemic fluorosis where health problems abound.

Dr. Najera is planning a third article discussing the biostatistical issues with the research – a very important issue I have commented on in previous posts. I look forward to it and will do a post on it in due course.

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An evidence-based discussion of the Canadian fluoride/IQ study

Dr. Christopher Labos and Jonathan Jarry discuss the recent Canadian fluoride/IQ research. They provide an expert analysis of the paper and its problems. Click on image to go to podcast.

The critical debate about the recent Candian fluoride/IQ study is continuing. Dr. Christopher Labos and Jonathan Jarry of The Body Of Evidence group discussed the research in their recent Podcast. The subject is appropriate because, as their website says:

“The Internet allows fantasies to thrive, but your health (and wallet) should not be the target of nonsense. There is a body of evidence out there, and Dr. Christopher Labos and Jonathan Jarry are staring it right in the face.

Through a podcast, a shared blog, and videos (and even appearances on the radio and in person), they explore what reproducible evidence has to say on important medical topics, and how scientific thinking shouldn’t be the sole purview of researchers. The bickering is just the cherry on top.”

The discussion is in Podcast 053 – Smart Drugs and Fluoride. The section on the Canadian fluoride/IQ study starts at 30 minutes and is 10 minutes long.

It’s a very thorough discussion going into a range of problems with these sort of studies and problems with the particular study. It raises the issue of differing results obtained by similar studies (eg the Mexican study did not report differences between boys and girls although the Canadian study did see Paul Connett’s misrepresentation of maternal F exposure study debunked and other articles here). They also discuss important factors the Canadian study ignored

Clearly, there is a lot wrong with the Canadian study – or at least a lot of factors that a sensible reader should take into account.

An important issue is the ethics of publishing controversial studies like this. In particular, the authors should have been aware that their results would be used by anti-fluoride activists to scaremonger in their campaign against community water fluoridation (that is certainly happening in New Zealand). And the most effective scaremongering is raising fears about children. Christopher and Jonathan suggest that in such a situation the authors should have been responsible enough to do further work to eliminate doubts or at least present their findings in a more qualified way. The authors should have been more diligent considering the way their findings were going to be used by activists.

The fact that this was not done suggests to me that other factors, such as professional ambition and pressure form immediate peers and their institution came into play (see Politics of science – making a silk purse out of a sow’s ear).

They also finish with a discussion of the nature of IQ tests and suggest that the differences claimed by the researchers are rather meaningless given that the average IQ of the children in the study were above average.

There is a very strong message here for the non-specialist. In cases like this, one should never simply accept the initial claims because they can be highly motivated. Christopher and Jonathan recommend that non-specialists should wait several days for the more balanced views to be published. There are plenty of experts out there who can provide this balance – they just have to be given time to actually read the paper, work out what the data means and how that compares with the claims made by authors.

For other comments on the Candian fluoride/IQ research see:

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More expert comments on the Canadian fluoride-IQ paper

The Green et al (2019) fluoride/IQ is certainly controversial – as would be expected from its subject (see If at first you don’t succeed . . . statistical manipulation might help and Politics of science – making a silk purse out of a sow’s ear). Anti-fluoride campaigners have been actively promoting it as the best thing since sliced bread. But it has not received the same glorification from most experts.

The UK Science Media Centre published a list of reactions from experts (see expert reaction to study looking at maternal exposure to fluoride and IQ in children) and they are worth reading. Also, there are comments from Dr F. Perry Wilson presented in the video above.

Wilson raises some very valid criticisms, a few of which (such as the weakness of the reported relationship) I have dealt with. He also brings attention to how even that weak relationship appears to be strongly affected by extreme values (particularly the few high values for maternal urinary fluoride).

He says:

But as you can see from the scatter plot, the effect was really small—about 1.5 IQ points for moving between the 25th and 75th percentiles of urinary fluoride.”

This is from the data presented in the paper’s Table 2. This low value was not really discussed by the authors who instead promoted an effect of 4.5 IQ points using data covering the whole range, including the unrealistically high urinary F values.

I have already commented in the previous articles on how weak the child IQ – maternal Urinary Fluoride relationship is and how it has no predictive value (see If at first you don’t succeed . . . statistical manipulation might help and Politics of science – making a silk purse out of a sow’s ear). Also, as the mean IQ values for all children and separately for boys and girls are not affected by the residence of mothers in fluoridated and unfluoridated areas it is likely that even this weak relationship is anomalous.

However to return to Dr F. Perry  I have reproduced the transcript of his talk below as he does make a number of valid points worth considering.


Welcome to Impact Factor, your weekly dose of commentary on a new medical study. I’m Dr F. Perry Wilson.

Usually, as studies come across my desk, I say, “Oh, that one is interesting” and dig in to see if it’s worth spending a few minutes of your time on. This week, I saw this study appearing in JAMA Pediatrics and just thought, “Oh no.”

This is one of those studies that I just knew would blow up, and probably for the wrong reasons.

Despite robust evidence that fluoridation of water reduces the incidence of cavities in kids, it has long been a bugaboo of conspiracy buffs, ranging from General Jack D. Ripper to Alex Jones.

No one ever seems to complain about chlorinating water, but whatever.

In any case, the argument that fluoridation is a secret communist plot has never held water, but several prior medical studies have documented a link, however small, between fluoride exposure and lower IQ in children. But all of those prior studies were flawed in one way or another, most often because the exposure was orders of magnitude higher than what is seen in the fluoridated water supply.

Enter JAMA Pediatrics, with what is really the best study to date of the effect of fluoride on IQ. Five hundred and twelve mother-child pairs from Canada were recruited during pregnancy and followed until the kids were around 3-4 years of age. At three points during pregnancy, the moms had their urinary fluoride concentrations measured. These measurements were averaged, and the researchers report that moms with higher levels had kids with lower IQs.

This held up even after adjusting for city, maternal education, race, child sex, and a score that measures the quality of the home environment.

Yikes, right? Is fluoridation causing a process of “dumbening”? Is “dumbening” even a word?

Hold up. It’s caveat time.

First, this was not a randomized trial. No one was giving these moms fluoride or regulating what they drank, so confounders could be a major issue. I’m particularly worried about socioeconomic factors that may be linked to fluoridated water consumption and also children’s IQ.

But there’s potentially a bigger problem. The plausible mechanism for neurotoxicity of fluoride in utero is that blood fluoride crosses the placenta and gets into the fetus’ developing brain. Like this.


But blood fluoride wasn’t measured. Urine fluoride was. Now, as a nephrologist, this piques my interest because urine fluoride is not a perfect proxy for blood fluoride.


The authors know this. They realize, for example, that more dilute urine will have a lower fluoride concentration, and they “correct” this problem by dividing urinary fluoride by urine specific gravity.


But this introduces a new variable. Assuming that fluoride has no effect on a child’s IQ, you could get results that look like this if moms with more dilute urine tend to have kids with lower IQs.

But wait, there’s more. Fluoride is freely filtered at the glomerulus, but it is reabsorbed in the renal tubules. This is a pH-dependent process,[2] with more reabsorption occurring when the pH is lower. That means that women with higher urinary pH (due to a more vegetarian diet or just prolonged fasting) will, on average, have higher urinary fluoride levels.


Another confounder, this one unaccounted for.

Does the presence of possible confounders invalidate the study? Of course not, but these factors remind us to interpret results like this very carefully, especially when the documented benefits of fluoridation rest on much firmer scientific footing than the possible harms.

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