Category Archives: science

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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Politics of science – making a silk purse out of a sow’s ear

Anti-fluoride activists have some wealthy backers – they are erecting billboards misrepresenting the Canadian study on many New Zealand cities – and local authorities are ordering their removal because of their scaremongering.

Many New Zealanders are concerned at the scaremongering by anti-Fluoride activists lately. Media commenters have criticised misleading advertisements placed in national and local newspapers by Fluoride Free New Zealand (FFNZ). And local authorities have ordered some of the billboards erected by FFNZ to be taken down because of their scaremongering (see Anti-fluoride billboard removed).

A misleading advertisement anti-fluoride activists are inserting in many New Zeland newspapers. Media commentators believe newspapers must allow space for the experts to present the true science listen to Gavin Ellis, Nine to Noon, Sept. 3).

I can fully understand anti-fluoride activists taking advantage of current interest in the recently published Canadian fluoride/IQ study (see If at first you don’t succeed . . . statistical manipulation might help). They are, after all, an advocacy group, well known for their misrepresentation of the science, and they have rich backers in the alternative health industry (see Big business funding of anti-science propaganda on health and Anti-fluoridationists go to Supreme Court – who is paying for this?) to finance this expensive advertising.

As always its a matter of “Reader Beware.”  In this day and age readers are well-advised to take what they see on billboards and in newspapers (particular advertisements) with large dollops of salt.

“Sexy” science

But what disturbs me is the role the personal ambition of scientists and institutional advocacy, the politics of science, has played in this unfortunate misrepresentation and advocacy of the Canadian study.

This is not an isolated case. Scientific researchers with any experience know of many examples where individual scientists have misrepresented or exaggerated their findings because of personal ambition and institutes have gone along with this to obtain kudos (and money). We all know of young scientists who have behaved in this way, effectively promoting their poor science, risen through the ranks and, before long, exited research to take high-paying roles in science administration. More honest researchers are often scathingly critical of such ambition – especially as we often have to put up with these people manipulating funding and distorting scientific directions when they later become administrators. At the same time, we respect the honest researchers who often plod away, making slow but real progress, but not seeking, or getting, full public recognition for their work – because it is not “sexy” enough.

Yes, I like most researchers, have experienced administrators demanding that we make our research more “sexy.” These same administrators have never expressed the desire we make out research more honest, more professional or more effective (except in terms of winning publicity and unthinking funds).

Relying on “authority” statements while ignoring real data

Interestingly, in this current round of scaremongering the anti-fluoride activists do not reproduce data from the studies. There are no graphs, for example. Perhaps the real-life data is so scattered it would expose their claims of specific values of harm as fantasy.

According to an FFNZ post, the IQ of children in this study would have dropped by 4.5 points, from 114.1 where mother’s urinary F concentration (MUF) was 0 mg/L to 109.6 when the MUF was 1 mg/L. But look at the figure below. Very few of the data points near a MUF of 1 mg/L are actually near 109.6. They vary from about 75 to 125! No wonder they do not reproduce the figure from the paper to support their claim.

Instead, they rely on the approach of quoting the statements of “authorities,” some of the authors and others who have commented favorably on the research.

Yes, the authors did state they had found an association (for boys) with a best-fit line (the blue line above) showing an IQ decline of 4.49 points for an increase in MUF of 1 mg/L. But clearly this is meaningless when the very high scatter of the data is considered – it has no predictive value. This is because the claimed IQ reduction represents only the best fit line, the very weak association of IQ with MUF they reported. An association so weak that it refers only to 1.3% of the data (see If at first you don’t succeed . . . statistical manipulation might help).

OK, perhaps I can be accused of ignoring the stated variability of the reported relationship (although so have the anti-fluoride campaigners). Green et al (2019) described the relationship in the abstract of their paper this way:

“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.”

As a predictor that is saying that 95% of IQ values for boys at a MUF of about 1 mg/L should be in the range 105.7 to 113.5. Again, simply not true (look at the figure above). In fact, this relationship refers only to the best fit line and the blue zone in the figure above indicates where this line could go 95% of the time.

So this “authority” statement about the reported relationship is simply of no practical value as it applies to only 1.3% of the data. It is meaningless and it’s irresponsible for the authors and other “authority” spokespeople to refer to this relationship in the way they have without mentioning how weak it is.

The “authority” statements and those of the authors themselves are doubly worse because they ignore the fact that there is no statistically significant difference for the IQs of all children and separately the boys and girls, for mothers who lived in fluoridated and unfluoridated areas during their pregnancy. The data showing this is in Table 1 of the paper (and presented below) so it is strange that the authors did not discuss this in their paper at all.

Mean IQ of children whose mothers drank fluoridated or unfluoridated water during pregnancy (SD =  11.9 – 14.7)

Nonfluoridated Fluoridated
All children 108.07 108.21
Boys 106.31 104.78
Girls 109.86 111.47

Shameless advocacy by scientists and institutions

Scientists are only human and it’s perhaps not surprising that authors of these and similar studies will exaggerate the importance of their finding and remain silent about deficiencies in their studies. After all, self-promotion of this sort, especially if it gets widespread industry and public attention, can only be good for their careers.

One of the authors, Cristine Till is reported as saying:

“At a population level [4.5 IQ points, SC], that’s a big shift. That translates to millions of IQ levels lost” [reported by CNN]

And:

“We would feel an impact of this magnitude at a population level because you would have millions of more children falling in the range of intellectual disability, or an IQ of under 70, and that many fewer kids in the gifted range…We recommend that women reduce their fluoride intake during pregnancy.” [Reported by NPR]

And:

“Four and a half IQ points is of substantial societal and economic concern…We’re talking a magnitude that’s comparable to what we’re talking about when we talk about lead exposure. You would have millions of more children falling into the range of intellectual disability with IQ scores of less than 70, and that many fewer kids in the gifted range.” [Reported by WebMD]

Looking at the graph we can clearly see that at the population levels there are not these huge losses in IQ. She omits the fact that the relationship they report is extremely weak. Her statement is misleading – but she no doubt feels its good for her career. She also omits the fact shown by their own Table 1 that there is no difference in IQ fo children whose mothers lived in fluoridated or unfluodiated areas.

And then we get promotion of these misrepresentations by other scientists – apparently independent of the authors but a closer look shows them to be linked.

This from  Phillipe Grandjean:

“This is an excellent study,”  . . . CDC has to come out and look at the risk-benefit ratio again, because they can’t continue relying on studies that were carried out decades ago.” (Reported by Washington Post]

Grandjean frequently makes these sort of comments on studies which can be interpreted as supporting anti-fluoride positions. He is even a bit of a go-to spokesperson for the Fluoride Action Network (FAN) and his bias is clear, despite his professional standing. As the chief editor for Environmental Health he would not allow my critique of the Malin & Till (2015) ADHD study to be considered for publication (see Fluoridation not associated with ADHD – a myth put to rest). My critique was later reviewed and accepted by a different journal but ethically it should have been published by the journal which published the original Malin & Till (2015) paper.

Grandjean was one of the people I commented on in my articles about the poor peer review of the Malin & Till paper (see Poor peer-review – a case study and Poor peer review – and its consequences). So was David Bellinger – a subeditor who dealt with the Malin & Till (2105) paper. Bellinger, coincidentally, wrote a supportive opinion piece on the Green et al (2019) paper in the issue of JAMA  Pediatrics which published the Green et al paper.

It amazes me sometimes how incestuous journal editors, paper authors and peer reviewers cna be.

Don’t get me wrong. there was also quite a widespread criticism of the Green et al (2019) paper in the scientific community. Hopefully, some of these critics will contribute critiques of the paper to the journal. Also, hopefully the journal editor will allow these critiques to be published (although, as I point out in If at first you don’t succeed . . . statistical manipulation might help the editor seems only to be welcoming debate on these findings if it is in the public media).

The point, of course, is that activist anti-fluoride organisations like FAN and FFNZ never quote those critics. they simply quote the apparently “authoritative” figures who have praised the paper or promoted the misinformation described above. Thes misleading quotes from authors, institutes and “authority” supporters are simply mana from heaven to the FAN and FFNZ activists.

Dangers of science politics allied with ideologically-motivated advocacy

Problems with the politics of science occur all the time. That is why I always suggest readers should look at papers, even those in the most reputable journal, critically and intelligently. Importantly, one should look at the actual data where possible to check that it is portrayed properly in the discussion, conclusions and abstract. Otherwise, it is easy to be misled by ambitious authors, public relations press releases from institutions and “independent” scientific commenters.

Also, only the most naive reader accepts information in news articles and adverts as necessary gospel truth. The sensible reader approaches the media and advertising critically.

However, in this case, I think people have a right to be far more concerned about the misinformation – both that presented by authors, their institutes and their colleagues, and the way it is presented to us by the media and advertising. This is because the worst sort of fear-mongering is involved – playing on our love for our children and inherent wishes to do anything to protect them.

So I support the concern expressed by people in the media, the suggestion that such advertising should be accompanied by more informed articles, and the actions of local authorities in ordering the offending billboards be taken down. I passionately believe in free speech and defend the rights of even those I think wrong to partake of this – to express their opinions. But when the wellbeing and health of children and their parents are compromised by that free speech I think there is a case for it to be limited.

After all, there are other avenues, especially one which enable claims to be challenged properly, where that free speech can continue without creating harm.

Science reporters should be more responsible

I can’t finish without airing a concern I have about science reporters. In a sense, they are in a privileged position because the subjects they deal are credible because science is involved. Science does have a reputation for getting to the truth and being objective.

Privilege is also conferred on these reporters because they continually cite and quote experts – people of “authority” because of their education and scientific positions.

But surely an experienced science reporter is aware of the problems of politics in science – the role of ambition and search for fame which can lead to misrepresentations or at least over-glorification of the subjects being reported by the researchers involved. They must also surely be aware that different schools of thought within the scientific community can lead to biased presentations.

It’s not good enough for science reporters to simply quote authors and biased researchers. Surely they have an obligation to do some checking on the credibility of the claims being made. Why not read the full papers rather than rely on an abstract or an author’s claim? Further, and more importantly, why not cast a critical eye over the evidence reported in the paper and the data if it is there? Surely most science reports have some scientific and statistical skills.

I realise reporters have deadlines. In this case, some of the “authorities” commenting on the paper had been given prior access – a bit unfair for other reporters. It is tempting to go with what one has and not delay an article by indulging in critical analysis. But reporters should also think responsibly. Misinformation, in this case, is being used to promote dangerous scaremongering. That scaremongering should not be assisted by negligent reporting.

I am saddened that hardly any reporters quote the important information from the paper – that presented in the paper’s Table one and the table above. Differences in IQ of children whose mothers lived in fluoridated or unfluoridated areas are not different – or more correctly the differences (changes of +0.14 points for all children, -1.53 points for boys and + 1.61 points for girls when fluoridation is involved) are not statistically significant.  OK, the authors and their promoters were silent about that data – but a good reporter should have picked it up.

On the other hand, most science reporters ignored the actual data and went with the quotes. So instead of the data in the table above a misleading IQ change of 4.5 points for boys was presented as the main message. With absolutely no evaluation of how weak the relationship used to obtain that figure is.

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If at first you don’t succeed . . . statistical manipulation might help

Anti-fluoride campaigners are promoting yet another new study they claim shows community water fluoridation lowers children’s IQ. For example, the Fluoride Free NZ (FFNZ) press release Ground Breaking Study – Fluoridated Water Lowers Kid’s IQs which claims the study confirms“our worst fears, linking exposure to fluoridated water during pregnancy to lowered IQ for the developing child.”

Yet the study itself shows no significant difference in children whose mothers lived in fluoridated or unfluoridated areas during pregnancy. Here is the relevant data from Table 1 in the paper:

Mean IQ of children whose mothers drank fluoridated or unfluoridated water during pregnancy (SD =  11.9 – 14.7)

Nonfluoridated Fluoridated
All children 108.07 108.21
Boys 106.31 104.78
Girls 109.86 111.47

The differences between fluoridated and nonfluoridated are not statistically significant.

The paper has just been published and 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.

Surprisingly the authors do not discuss the data in the table above. Its as if the data didn’t exist, despite being given in their Table 1. I find this surprising because their discussion is aimed at finding a difference – specifically, a decrease in child IQ due to fluoridation – and surely these mean values must be relevant. Were the authors embarrassed by these figures because they did not show the effect they wanted?

So how did they manage to find an effect they could attribute to fluoride, or fluoridation, despite the mean values above? They basically resort to statistical manipulation – and this has opened up an intense controversy about the paper.

An unprecedented “Editor’s Note”

The journal editor, Dimitri A. Christakis, published a note alongside the paper (see Decision to Publish Study on Maternal Fluoride Exposure During Pregnancy), together with a piece in the Opinion section by David C. Bellinger (see Is Fluoride Potentially Neurotoxic?). This opinion piece is described as an editorial although Bellinger is not an editor of the journal or on the Editorial Board.

This is, in my experience, completely unprecedented. Editor’s don’t comment on the quality of papers or the refereeing process and I can only conclude that within the journal editorial board and those who reviewed the paper there were sharp differences about its quality and whether it should be published. While an editorial may sometimes bring attention to an article, in this case, it is likely that Bellinger was one of the reviewers of the paper and he is expressing his viewpoint on it and supports its publication.

Christakis writes “The decision to publish this article was not easy.” He goes on to imply the journal supports publication “regardless of how contentious the results may be.”  But surely there is no need to defend a good quality paper in this way just because the results may be “contentious.”

Interestingly, FFNZ interpreted the publication of the Editor’s note as making the publication of the paper more “impactful” not realising that the Note is probably not positive for the paper as it reveals controversy over the paper’s quality and whether it was worthy of publication. FFNZ also chose to describe Bellinger’s comments in his opinion piece as representing the views of the authors. However, it would be inappropriate for an editor to make such comments.

I think Bellinger has his own biases and preferences which lead him to advocate for papers like this. I commented on Bellinger’s role in the review of another paper promoting an anti-fluoride perspective in my articles Poor peer-review – a case study and Poor peer review – and its consequences.

A large amount of controversy

I am surprised at the degree of controversy around this paper – and it’s loudness. The fact that it started on the same day the paper was made public reveal various actors have had access to the paper and have been debating it for some time.  This could have been stoked by the unorthodox statistical analysis used and contradictions in the findings.

But it appears this controversy had gone far wider than the journal editors and reviewers of the paper because of the immediate reactions from anti-fluoride organisations like the Fluoride Action Network (see BREAKING: GOVERNMENT-FUNDED STUDY LINKS FLUORIDATED WATER DURING PREGNANCY TO LOWER IQS IN OFFSPRING), some leading Newspapers,  professional bodies (see AADR Comment on Effect of Fluoride Exposure on Children’s IQ Study) and the UK Science Media Centre which published a reaction from experts article (see expert reaction to study looking at maternal exposure to fluoride and IQ in children).

This suggests to me a large degree of lobbying. Not only from activists and anti-fluoride scientists or reviewers. But also from authors and their institute. I am not really surprised as I have often seen how politics, activism, commercial interests, and scientific ambitions will coordinate in these situations.

How to discover an effect from a nonsignificant difference

So how do we get from the data in the table above – showing no statistically significant difference between fluoridated and unfluoridated areas – to a situation where the authors (who don’t refer to that data in their discussion) say:

“higher levels of fluoride exposure during pregnancy were associated with lower IQ scores in children measured at age 3 to 4 years. These findings were observed at fluoride levels typically found in white North American women. This indicates the possible need to reduce fluoride intake during pregnancy.”

In their press releases and statements to media, where they are not constrained by a journal’s need for evidence and objectivity, they come out even more vocally against community water fluoridation.

Well, it appears to me, by statistical manipulation. One of the Science Media experts referred to above, Prof Thom Baguley, wrote:

“First, the claim that maternal fluoride exposure is associated with a decrease in IQ of children is false. This finding was non-significant (but not reported in the abstract). They did observe a decrease for male children and a slight increase in IQ (but non-significant) for girls. This is an example of subgroup analysis – which is frowned upon in these kinds of studies because it is nearly always possible to identify some subgroup which shows an effect if the data are noisy. Here the data are very noisy.”

It appears the authors found a significant effect of child sex on IQ so made a decision to do a subgroup analysis – of boys and girls – and this produced a significant association of IQ with maternal urinary fluoride for the boys. This resort to subgroup analysis may have, in itself, produced a misleading significant relationship.

Adam Krutchen, Biostatistics PhD student at the University of Pittsburgh, also illustrates how the relationship with child sex has confused the analysis. He comments on the data that he managed to extract from the paper’s Figure 3:

“There were drastic sex-specific IQ differences in the children, which is of course strange. We shouldn’t expect anything like that to happen. This difference is very significant. There’s also some outlier extremely low IQ values among the male children.”

He is saying that his regression analysis showed a strong effect of child sex on IQ. This is quite irrespective of maternal urinary F or drinking water F. However, once that effect of child sex is taken into account he found no relationship of child IQ with maternal urinary F. He says:

“with such a significant effect of sex on IQ, does fluoride have any remaining relationship? The answer is a resounding no in the digitized data.”

It appears that including child sex difference in the regression analysis produces the finding that there is no significant relationship of fluoride to child IQ after taking into account the significant relationship of IQ with child sex. But when the data is divided into subgroups and analysed separately (a technique statisticians “frown on” “because it is nearly always possible to identify some subgroup which shows an effect if the data are noisy”) a significant relationship of IQ with maternal urinary fluoride can be produced for boys (but not girls).

Interestingly, a second part of the Green et al., (2019) study investigated a relationship of child IQ with unverified estimated fluoride intake by the pregnant mothers. The estimation method was not verified so may be questionable). No sex difference appeared in that data set.

How strong are the reported relationships

Perhaps it is not necessary to go any further. Perhaps the data for mean IQ in the table above is sufficient to show there is no effect of fluoride on IQ. Or perhaps the critique of the analysis of subgroups used is sufficient to make the reported conclusions suspect.

However, perhaps a comment on the weakness of the relationships reported by Green et al is useful – if only because I took the trouble to digitally extract the data from the figures in the paper and do my own regression analyses on the data.

Of course, digital extraction does not get all the data – even if only because the points may merge. In this case, I managed to extract 410 data points from Figure 3A which showed the relationships of child IQ with the maternal urinary F concentrations during pregnancy. This is quite a bit smaller than the 512 data pairs the authors reported in their Table 1 and suggests to me they had not plotted all their data. However, the values for means and coefficients obtained by my own regression were very similar to those reported by Green et al., (2019).

The authors reported a significant (p=0.02) negative relationship of boy’s IQ with maternal urinary F. They do not discuss how strong that relationship is – although the wide scatter of data points in the figures suggest it is not strong. My regression analysis showed the relationship explained only 1.3% of the variance in IQ. I do not think that is worth much. With such low explanatory power, I think the authors overstate their conclusions.

I think this is another case of placing far too much reliance on p-values and ignoring other results of the statistical analysis. I discussed this in a previous article – see Anti-fluoride activists misrepresent a new kidney/liver study).

Conclusions

I think this paper has been overblown. It has problems with its statistical analyses as well as other limitations referred to in the paper. I do not think it should have been published in its present form – surely reviewers should have picked up on these problems. I can only conclude that intense arguments occurred within the journal’s editorial board and amongst reviewers – and most probably more widely amongst institutes and activist groups. In the end, the publication decision was most likely political.

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Anti-fluoride activists misrepresent a new kidney/liver study

Image Credit: Wild Rose College

A new study reporting the ranges of values for kidney and liver parameters in a healthy population is being actively misrepresented by anti-fluoride campaigners. The Fluoride Action Network’s (FAN) latest bulletin claims the study shows “that fluoride at commonly experienced doses can damage the kidneys and livers of adolescents.”

The study shows nothing of the sort. How could it – individuals suffering liver or kidney disease were specifically excluded from the study population. The reported parameter values are all for healthy individuals.

Readers can check for themselves – there is a free download. The paper is:

Malin, A. J., Lesseur, C., Busgang, S. A., Curtin, P., Wright, R. O., & Sanders, A. P. (2019). Fluoride exposure and kidney and liver function among adolescents in the United States: NHANES, 2013–2016. Environment International,

It is important to understand what this study really found. Not only is it being misreported by anti-fluoride activists. The University (The Mount Sinai Hospital/Mount Sinai School of Medicine) press release also appears to attribute more to the study’s findings than is warranted. This is a common problem with university public relations departments. (Readers are warned – the press release includes the disclaimer:

“AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system”

Below I list some information on the study

This is not a study about kidney or liver disease

Individuals showing such disease were specifically excluded. The study reports values for kidney and liver parameters in “generally healthy” subjects. The authors make this very clear in the discussion saying:

“this study did not aim to determine whether fluoride exposure is associated with clinical decrements in kidney function among U.S. adolescents. Rather, this study aimed to examine subclinical changes in kidney or liver parameters associated with fluoride exposure among a generally healthy population. For example, the lowest GFR estimated in this study was 84 mL/min/1.73m2, and therefore none were below the<75 mL/min/1.73m2 value considered reflective of
abnormal kidney function. Future prospective studies including participants with and without kidney disease are needed to assess clinical changes in kidney or liver function.”

So, this study just could not have identified factors causing kidney and liver disease, let alone confidently attribute a cause to the disease. So we can reject the anti-fluoride activist’s claims and their misrepresentation of the study results.

But why all this fuss about fluoride?

Because the authors have a preoccupation with fluoride they used statistical analyses to see if they could find any association between drinking water fluoride or blood plasma fluoride and the measured kidney and liver parameters. They did find a small number of very weak associations.

They do not support the claims made by anti-fluoride activists so details of their results and a critique of their results are irrelevant to the main arguments. But I do have a hangup about the way statistical analyses are used, and the way they are over-interpreted to support pet biases so will discuss their data below.

Very few associations found

The study included nine kidney and liver function test parameters. Only one of these (Blood Urea Nitrogen [BUN]) had a statistically significant relationship with water fluoride (Uncorrected p <0.001) – see figure below.

The relationship of BUN with blood plasma F was also statistically significant (Uncorrected p <0.001) – see figure below.

The Standard Reference Range of BUN for this adolescent population is 6–23 mg/dL. Only a few data points are outside that range and they mainly occur for low water F or plasma F concentrations.

The authors also reported statistically significant associations of estimated glomerular filtration rate (eGFR) and Serum uric acid (SUA) with blood plasma F. However, once adjustments were made for plasma cotinine levels (a biomarker of tobacco smoke exposure) associations were not statistically significant (uncorrected p=0.18 for eGFR) or only “marginally” statistically significant (uncorrected p=0.06 for SUA).

In effect, statistically significant associations with either water F or plasma F occurred for only one. It is not credible for FAN to use these associations as indicators “that fluoride at commonly experienced doses can damage the kidneys and livers of adolescents.”

Reported associations may be “a pure act of will”

The authors appear to place a lot of reliance, in my opinion far too much reliance, of p values as somehow providing a causal mechanism behind the reported associations. This reliance has been strongly criticised by statisticians. Recently Briggs (2019) (Everything Wrong with P-Values Under One Roof) concluded:

“P-values should not be used. They have no justification under frequentist theory; they are pure acts of will. Arguments justifying p-values are fallacious. P-values are not used to make all decisions about a model, where in some cases judgment overrules p-values. There is no justification for this in frequentist theory. Hypothesis testing cannot identify cause. Models based on p-values are almost never verified against reality. P-values are never unique. They cause models to appear more real than reality.”

He goes on to elaborate:

“a small p-value has no bearing on any hypothesis . . . Making a decision about a parameter or data because the p-value takes any particular value is thus always fallacious . . . . Decisions made by researchers are often likely correct because experimenters are good at controlling their experiments, . . . . . ., but if the final decision is dependent on a p-value it is reached by a fallacy. It becomes a pure act of will.”

I believe Malin et al., (2019) place too much reliance on the p values they obtained and should have provided more complete results from the statistical analyses. Citing and relying on p values alone is, I believe, a major deficiency in this paper.

To their credit, while not providing full statistical analysis results the authors did display individual data points in their figures 1 and 2. This enables careful readers to make some judgments about the statistical analyses which would not be possible if the figures had not been provided.

Problems with outliers

The figures show a small number of outlying data points with some of the parameters. One has to be very careful that any association found only has a low p-value because of the influence (or leverage) of these outliers. The figures above for the BUN parameter illustrate the problem – particularly for water F where 2 data point greater than 6 mg/L clearly have a lot of influence.

This problem should stand out to any informed reader of the paper. The authors claim “Cook’s distance estimates were used to test for influential data points; none were identified.” However, this does not seem credible (particularly for Water F) so it is understandable that I should ask to see the results of these estimates so I can make up my own mind. They were not provided.

The associations were extremely weak

There is a huge scatter in the data points obvious in the figures above. This tells us that the reported associations can explain only a small amount of the variance. This is one reason why p-values alone can be misleading. A low p-value for an association (or fitted line) explaining only a few percent of the variance is meaningless. Concentration on such associations means that more important ones (explaining more of the variance) may be ignored. It also ignores the fact that the risk-modifying factor (in this case water F or plasma F) may simply be acting as proxies for more important factors (see Perrott 2018 for an example of this).

Malin et al., (219) should have provided more complete statistical analyses results to help readers judge the strength of the reported association. however, the figures themselves enable us to conclude the associations are very weak.

It is misleading to use the statistical result predictively

Malin et al., (2019) appear to “predict” the effect of fluoride on liver and kidney parameters, particularly BUN. They write in their abstract:

“A 1 mg/L increase in water fluoride was associated with a 0.93 mg/dL lower blood urea nitrogen concentration (95% CI: −1.44, −0.42; p=0.007)”

And

“1 μmol/L increase in plasma fluoride was associated with . . . . . a 1.29 mg/dL lower blood urea nitrogen concentration (95%CI: −1.87, −0.70; p < 0.001).”

But consider going from 0 to 1 mg/L in the image above for water F. The fitted line suggests that BUN would drop from about 11 to about 10 mg/dL. Taking the 95% CI interval into account the line “predicts” a value in the range of about 9.56 to 10.58 mg/dL. But only a small number of the points scattered at about 1 mg/L F have values in that range.

[Yes, I know. The authors only refer to associations, but reports of this work in the alternative health media are using these statements as predictions and that is how activists are suing the information.]

All that the best fit line can predict are values which fit the line. As the association represented by the best-fit line explains only a very small percentage of the variance (despite the small p-value) these “predictions” are meaningless. Unfortunately, the authors do not make this clear in their paper and this deficiency only contributes to the ability of anti-fluoride activists to misrepresent the findings.

Conclusions

Anti-fluoride activists are misrepresenting the finding reported in this paper. The authors themselves stress that their study was not designed to determine if fluoride exposure is associated with, or causes, declines in kidney or liver health. The FAN claim that the study shows“that fluoride at commonly experienced doses can damage the kidneys and livers of adolescents” is completely incorrect.

That is all we need to know regarding the way activists are misrepresenting the study. However, a closer look at the data suggests that the associations with fluoride for healthy individuals reported in the paper are extremely weak.

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