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 exerpt 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:

• Fluoride, pregnancy and the IQ of offspring, 
• Maternal urinary fluoride/IQ study – an update, 
• Paul Connett’s misrepresentation of maternal F exposure study debunked,
• Mary Byrne’s criticism is misplaced and avoids the real issues, and
• A conference paper on the maternal prenatal urinary fluoride/child IQ study has problems

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:

• If at first you don’t succeed . . . statistical manipulation might help
• Politics of science – making a silk purse out of a sow’s ear
• More expert comments on the Canadian fluoride-IQ paper
• An evidence-based discussion of the Canadian fluoride/IQ study
• Fluoridation – A new fight against scientific misinformation
• Biostatistical problems with the Canadian fluoride/IQ study

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!

Similar articles

Fluoridation and ADHD: A new round of statistical straw clutching

“To clutch at straws – the act of reaching for a solution no matter how irrational or inconsequential.” Source: Advanced Vocabulary for English Language Learners

Anti-fluoridation activists are promoting a number of new scientific papers they argue support their campaigns. But one has only to critically read these papers to see they are clutching at straws. Their promotion relies on an unsophisticated understanding of statistics and confirmation bias.

I will look at one paper here – that of Bashash et al., (2018) which reports an association between maternal prenatal urinary fluoride and prevalence of child ADHD.

The paper is:

Bashash, M., Marchand, M., Hu, H., Till, C., Martinez-Mier, E. A., Sanchez, B. N., … Téllez-Rojo, M. M. (2018). Prenatal fluoride exposure and attention deficit hyperactivity disorder (ADHD) symptoms in children at 6–12 years of age in Mexico City. Environment International, 121(August), 658–666.

I discussed an earlier paper  by these authors – Bashash et al., (2016) which reported an association between maternal neonatal IQ fluoride and child IQ – (also heavily promoted by anti-fluoride activists) in a number of articles:

• Fluoride, pregnancy and the IQ of offspring,
• Maternal urinary fluoride/IQ study – an update,
• Anti-fluoridation campaigners often use statistical significance to confirm bias,
• Paul Connett “updates” NZ MPs about fluoride?,
• Paul Connett’s misrepresentation of maternal F exposure study debunked,
• Mary Byrne’s criticism is misplaced and avoids the real issues,
• A conference paper on the maternal neonatal urinary fluoride/child IQ study has problems, and
• A more convincing take on prenatal maternal dietary effects on child IQ.

Promotion of the new paper by anti-fluoride activists suffers from the same problems I pointed out for their promotion of the earlier paper. In particular it ignores the fact that the reported relationships (between maternal neonatal urinary fluoride and cognitive measure for children in Bashash et al., 2016, and prevalence of child  attention deficit hyperactivity disorder – ADHD – in Bashash et al., 2018) were very weak and explain only a very small amount of the variation. This raises the possibility that the reported weak relationships would disappear if significant risk-modifying factors were included in the statistical analyses.

Bashash, et al., (2018)

Whereas the earlier paper considered measures of cognitive deficits in the children the current paper considers various measurements related to ADHD prevalence among the children. These include parent rating scales (CRS-R). Three were ADHD-related scales from the Diagnostic and Statistical Manual of Mental Disorders (DSM) (Inattention Index, Hyperactivity-Impulsive Index and Total Index [inattentive and hyperactivity-impulse behaviours combined]). They also include several other indexes related to ADHD.

A number of computer-assisted indexes (CPT-II) were also determined.

Most indices were not significantly associated with maternal prenatal urinary fluoride. However, the authors reported statistically significant (p<0.05) relationships for indices of Cognitive Problems + Inattention, ADHD Index, DSM Inattention and DSM ADHD Total.

The data and the relationships were provided in graphical form – see figure below – taken from their Figure 2:

There is obviously a wide scatter of data points indicating that the observed relationships, although statistically significant, explain only a small part of the variation in the indices.

So, just how good are the relationships reported by Bashash et al., (2018) in explaining the variation in these ADHD-related indices? I checked this out by digitally extracting the data from the figures and using linear regression analysis.

Index	% Variance explained
Cognitive problems + Inattention	2.9%
ADHD Index	3.1%
DSM Inattention	3.6%
DSM ADHD Total	3.2%

In fact, these relationships are extremely weak – explaining only a few per cent of the observed variation in the ADHD related indices. This repeats the situation for the cognition-related indices reported on the Bashash et al., (2016) paper (see Maternal urinary fluoride/IQ study – an update).

The fact these relationships were so weak has two consequences:

1. Drawing any conclusions that maternal neonatal fluoride intake influences child ADHD prevalence is not justified. There are obviously much more important factors involved that have not been considered in the statistical analysis.
2. Inclusion of relevant risk-modifying factors in the statistical analysis will possibly remove any statistical significance of the relationship with maternal urinary fluoride.

Credible risk-modifying factors not considered

Bashash et al., (2108) do list a number of possible confounding factors they considered. These did not markedly influence their results. however, other important factors were not included.

Nutrition is an important factor. Malin et al., (2108) reported a signficant effect of nutrition on cognitive indices for a subsample of the mother-child pairs in this study (see A more convincing take on prenatal maternal dietary effects on child IQ).

Their statistical analyses show that nutrition could explain over 11% of the variation in child cognitive indices indicating that nutrition should have been included as a possible risk-modifying factor in the statistical analyses of Bashash et al., (2016) and Bashash et al., (2018). I can appreciate that nutrition data was not available for all the mother-child pairs considered in the Bashash et al., papers. However, I look forward to a new statistical analysis of the subset used by Malin et al., (2108) which includes prenatal maternal urinary fluoride as a risk-modifying factor and tests for relationships with child ADHD prevalence.

Could the reported weak relationship disappear?

Possibly. After all, it is very weak.

The problem is that urinary fluoride data could simply be a proxy for a more important risk-modifying factor. That is, urinary fluoride could be related to other risk modifying factors (eg. nutrition) so that the relationship with urinary fluoride could disappear when these other factors are included.

I illustrated this for a earlier reported relationship of child ADHD prevalence with extent of fluoridation in US states (see Perrott 2017 – Fluoridation and attention deficit hyperactivity disorder – a critique of Malin and Till (2015)). In  that case the relationship was much better than those reported by Bashash et al., (2016) and Bashash et al., (2018) – explaining 24%, 22% and 31% of the variance in ADHD prevalence for the years 2003, 2007 and 2011 respectively. The relationships are illustrated in their figure:

Relationships between water fluoridation (%) and child ADHD prevalence for 20013 (red triangles), 2007 (blue diamonds) and 2011 (purple circles). Malin & Till (2105)

Yet, when other risk-modifying factors (particularly mean state elevation) not considered by Malin & Till (2015) were included in the regression analyses there was no statistically significant influence from fluoridation prevalence. In this case fluoridation prevalence was related to altitude and was simply acting as a proxy for altitude in the Malin & Till (2015) regression.

Conclusion

As the authors admit, this study:

“was not initially designed to study fluoride exposure and so we are missing some aspects of fluoride exposure assessments (e.g., detailed assessments of diet, water, etc.).”

However, they do say these “are now underway” so I look forward with interest to the publication of a more complete statistical analysis in the future.

There are other problems with the data (for example the paucity and nature of the urinary fluoride measurements) and these are the sort of issues inevitably confronting researchers wishing to explore existing data rather than design experimental protocols at the beginning.

Readers should therefore always be hesitant in their interpretations of the results and the credibility or faith that they put on the conclusions of such studies. The attitude should be: “that is interesting – now let’s design an experiment to test these hypothetical conclusions.”

The problem is confirmation bias – the willingness to give more credibility to the findings than is warranted. Scientists are only human and easily succumb to such biases in interpreting their own work. But this is even more true of political activists.

The reported relationships are weak. Important risk-modifying factors were probably not included in the statistical analyses. The observed relationships may simply mean that urinary fluoride is acting as a proxy for a more important risk-modifying factor (like nutrition) and the weak relationship may disappear when these are considered.

So scientific assessment of this study will be extremely hesitant – interpreting it, at best, as indicating need for more work and better designed research protocols.

But, of course, political activists will lap it up. It confirms their biases. Political activist organisations like the Fluoride Action Network are heavily promoting this paper – as they did with the earlier Bashash et al., (2016) paper.

But they are simply clutching at straws – as they often are when using science (or more correctly  misrepresenting and distorting the science) to support their political demands.

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A more convincing take on prenatal maternal dietary effects on child IQ

Image credit: Nutrition and Pregnancy: Choline For Baby’s Development

Prenatal maternal nutrition is more likely to influence child cognitive abilities than fluoride. A new paper shows this by considering the effects of good or bad prenatal nutrition for the women in the Basash et al., (2016)  study that anti-fluoride campaigners promote. The new data shows that nutrition is more important than fluoride.

The Bashash et al. (2016) reported a weak relationship between prenatal maternal urinary fluoride and child cognitive outcomes or IQ (see Fluoridation: “debating” the science?). Anti-fluoride campaigners latched on to the paper because it seems to offer critical “evidence” for their claims that community water fluoridation lowers IQ. They argue that IQ, rather than the risk of dental fluorosis, should be the main consideration when considering community water fluoridation.

But a new study shows that prenatal maternal nutrition is a better predictor of neurodevelopmental outcomes for children than is urinary fluoride. This study used data from the same set of Mexican women/child pairs as Bashash et al., (2016).

Here is the citation for the new study:

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.

Misrepresentation  of the Bashash et al., (2016) study

I have dealt with this in a number of articles. Basically my argument was not with the study itself (although it obviously lacks consideration of important risk-factors in it statistical analysis) but with the way anti-fluoride activists use it to draw unwarranted conclusions.

A key problem they ignore is that the relationships reported by Bashash et al., 2016 can explain only about 3% of the variation in the cognitive measurements. This strongly suggests that the relationship with prenatal urinary fluoride would probably disappear if more important risk-modifying factors were included in the statistical analysis. My article “Predictive accuracy of a model for child IQ based on maternal prenatal urinary fluoride concentration.”  explains this and is available online.

The new Malin et al., (2108) study now provides some risk-modifying factors, specifically diet, which explains the data better than does urinary fluoride.

Readers wishing to refer back to my earlier posts on misrepresentation of the Bashash et al., (2106) study can read:

• Fluoride, pregnancy and the IQ of offspring, 
• Maternal urinary fluoride/IQ study – an update, 
• Paul Connett’s misrepresentation of maternal F exposure study debunked,
• Mary Byrne’s criticism is misplaced and avoids the real issues, and
• A conference paper on the maternal neonatal urinary fluoride/child IQ study has problems

Diet as a predictor of neurodevelopmental outcomes

The statistical analyses in this new paper are quite complex because the authors considered nutrient mixture and not simply each nutrient in isolation. Their argument for this is that we consume nutrients as mixtures and that interactions between nutrients is always possible.

The study, therefore, looked at the relationship of different neurodevelopmental outcomes in the children with prenatal maternal diet. Initially the authors considered the predictive ability of nutrition by considering “good” or “bad” diets based on U.S. dietary guidelines.

A bad diet during pregnancy may harm your future child’s neurodevelopment. Credit: © ivanmateev / Fotolia

Good maternal prenatal nutrition had a significantly positive effect on all the neurodevelopmental outcomes measured. In contrast, poor nutrition had a significantly negative effect on all the outcomes (see table below). Weighted Quartile Sums (WQS) were used to create indices for the individual diets.

I compared the predictive ability of prenatal maternal nutrition used here with the prenatal maternal urinary F approach used by Bashash et al., (2016) using data digitally extracted from their supplemental figures (S1 and S2 – see below). This was for the verbal development score of the children. Unfortunately, this was the only individual data presented.

Clearly, there is a lot of scatter in the data – to be expected where a number of risk-modifying factors are involved. However, the data showing a positive effect of good maternal prenatal nutrition on the verbal score of the children explains 7.1% of the variation. The data for poor prenatal nutrition explains 11.2% of the variation.

Compare this with the predictive ability of the data present by Bashash et al., (2016) where maternal prenatal urinary fluoride could only explain 3% of the variation of the child cognitive scores (see Maternal urinary fluoride/IQ study – an update).

Malin et al., (2018) were able to show which nutrients contributed most to the positive or negative neurodevelopmental outcomes of the children. They concluded:

“mothers who consumed more nutritious diets during pregnancy tended to have children with more favorable neurodevelopmental outcomes, while mothers who consumed less nutritious diets and/or higher levels of sodium, saturated fat, and/or sugar during pregnancy tended to have children with poorer neurodevelopmental outcomes. This suggests that the consumption of more comprehensively nutritious prenatal diets favorably affects child  neurodevelopment, while the consumption of less comprehensively nutritious prenatal diets may hinder it.”

Individual nutrients affected specific neurodevelopmental factors but they reported that prenatal dietary thiamine, vitamin B6, monounsaturated fats, fibre and calcium had beneficial effects. In contrast, lower monounsaturated fat, lower thiamine, lower fibre and higher saturated fat were associated with lower neurodevelopmental scores for the children.

Conclusions

If anti-fluoride activists are really concerned about child IQ and other aspects of child neurodevelopment then they should be campaigning on the importance of nutrition during pregnancy and stop diverting us by scaremongering about community water fluoridation.

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A conference paper on the maternal prenatal urinary fluoride/child IQ study has problems

Image credit: Do new mothers doing a Ph.D. get enough support?

The anti-fluoride movement has certainly mobilised over the prenatal maternal urinary fluoride study which reported an association with child IQ. They see it as the best thing since sliced bread and believe it should lead to the end of fluoridation worldwide.

They also seem to be putting all their eggs in this one basket and have started a campaign aimed at stopping pregnant women from drinking fluoridated water (See Warning to Pregnant Women: Do Not Drink Fluoridated Water).

So I was not surprised to see a newsletter this morning from the Fluoride Action Network reporting another output from this study – a conference paper (most likely a poster) presented at the  3rd Early Career Researchers Conference on Environmental Epidemiology. The meeting was in Freising, Germany, on 19-20 March 2018.

I had been aware of the poster for the last week so had expected FAN to gleefully jump on it and start promoting it in their campaigns.

Here is a link to the abstract:

Thomas, D., Sanchez, B., Peterson, K., Basu, N., Angeles Martinez-Mier, E., Mercado-Garcia, A., … Tellez-Rojo, M. M. (2018). Prenatal fluoride exposure and neurobehavior among children 1-3 years of age in Mexico. Occupational and Environmental Medicine, 75(Suppl 1), A10–A10.

It’s only an abstract and it may be some time before a formal paper is published, if at all. Posters do not get much in the way of peer review and often not followed by formal papers.  So I can’t say much about the poster at this stage as I never like to make an assessment of studies on the basis of abstracts alone.

But, in this case, I have Deena Thomas’s Ph.D. thesis which was the first place the work was reported. If you are interested you can access it from this link:

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.

I will wait for a formal paper before properly critiquing the poster, but at the moment I find a big discrepancy between the Thesis conclusions and the conclusions presented in the poster abstract.

Thesis conclusions

In her work, Deena Thomas used the Mental Development Index (MDI) which is an appropriate way of determining neurobehavioral effects in young children.

She concluded in her thesis (page 37):

“Neither maternal urinary or plasma fluoride was associated with offspring MDI scores”

And (page 38):

“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.”

And further (page 48):

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

So – no association found of child MDI score with maternal prenatal urinary F concentrations.

Poster conclusions

But the poster tells a different story.

The abstract 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.”

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

What the hell is going on?

The data

Obviously, I do not have access to the data and she does not provide it in her thesis. But from her descriptions of the data in her thesis and her poster perhaps we can draw some tentative conclusions.

The table below displays the data description, and a description of the best-fit line determined by statistical analysis, in her thesis and her poster.

Information on data	Thomas Ph.D. Thesis	Conference abstract
Number of mother/child pairs	431	401
Maternal Urinary F range (mg/L)	0.110 – 3.439	0.195 – 3.673
Mean maternal urinary F (mg/L)	0.896	0.835
Model β*	-0.631	-2.40
Model p-value	0.391 – Not significant
95% CI for β		-4.38 to -0.40

*β is the coefficient, or slope, of the best-fit line

Conclusions

Apparently at least 30 data pairs have been removed from her thesis data to produce the dataset used for her poster. Perhaps even some data pairs were added (the maximum urinary F value is higher in the smaller data set used for the poster).

This sort of change in the data selected for the statistical analysis could easily swing the conclusion from no effect to a statistically significant effect. So the reasons for the changes to the dataset are of special interest.

Paul Connett claims this poster “strengthens” the findings reported in the Bashash paper.  He adds:

“This finding adds strength to the rapidly accumulating evidence that a pregnant woman’s intake of fluoride similar to that from artificially fluoridated water can cause a large loss of IQ in the offspring.”

But this comes only by apparently removing the conflicting conclusions presented in Deela Thomas’s Ph.D. thesis. We are still left with the need to explain this conflict and why a significant section of the data was removed.

To be clear – I am not accusing Thomas et al. (2018) of fiddling the data to get the result they did. Just that, given the different conclusions in her thesis and the poster,  there is a responsibility to explain the changes made to the dataset.

From the limited information presented in the poster abstract, I would think the scatter in the data could be like that seen in the Bashash et al. (2017) paper. The coefficient of the best fit line (β) is relatively small and while the 95% CI indicates the fit is statistically significant its closeness to zero suggest that it is a close thing.

However, let’s look forward to getting better information on this particular study either through correspondence or formal publication of a research paper.

Other articles on the Mexican study

Fluoride, pregnancy and the IQ of offspring,
Maternal urinary fluoride/IQ study – an update,
Anti-fluoridation campaigners often use statistical significance to confirm bias,
Paul Connett “updates” NZ MPs about fluoride?
Paul Connett’s misrepresentation of maternal F exposure study debunked,
Mary Byrne’s criticism is misplaced and avoids the real issues

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Mary Byrne’s criticism is misplaced and avoids the real issues

Image credit: BuildGreatMinds.Com

First, thanks to Mary Byrne and FFNZ for this response (see Anti-fluoride group coordinator responds to my article). Hopefully, this will help encourage some good faith scientific discussion of the issues involved in my original article (Paul Connett’s misrepresentation of maternal F exposure study debunked). I am pleased to promote such scientific exchange.

I will deal with the issues Mary raised point by point. But first, let’s correct some misunderstandings. Mary claimed I am a “fluoride promoter” and had “sought to discredit the study via his blog posts and tweets.”

1. I do not “promote fluoride.” My purpose on this issue has always been to expose the misinformation and distortion of the science surrounding community water fluoridation (CWF). I leave promotion of health policies to the health experts and authorities.
2. I have not “sought to discredit the study.” The article Mary responded to was a critique of the misrepresentation of that study by Paul Connett – not an attack on the study itself. This might become clear in my discussion below of the study and how it was misrepresented.

The study

The paper we are discussing is:

Bashash, M., Thomas, D., Hu, H., Martinez-mier, E. A., Sanchez, B. N., Basu, N., … Hernández-avila, M. (2016). Prenatal Fluoride Exposure and Cognitive Outcomes in Children at 4 and 6 – 12 Years of Age in Mexico.Environmental Health Perspectives, 1, 1–12.

Anti-fluoride activists have leaped on it to promote their cause – Paul Connett, for example, claimed it should lead to the end of community water fluoridation throughout the world! But this is not the way most researchers, including the paper’s authors, see the study. For example, Dr. Angeles Martinez-Mier, co-author and one of the leading researchers,  wrote this:

1. “As an individual, I am happy to go on the record to say that I continue to support water fluoridation”
2. “If I were pregnant today I would consume fluoridated water, and that if I lived in Mexico I would limit my salt intake.”
3.  “I am involved in this research because I am committed to contribute to the science to ensure fluoridation is safe for all.”

Was the reported association statistically significant?

Mary asserts:

“Perrott claims that the results were not statistically significant but his analysis is incorrect.”

That is just not true. I have never claimed their reported association was not statistically significant.

I extracted the data they presented in their Figures 2 and 3A and performed my own regression analysis on the data. This confirmed that the associations were statistically significant (something I never questioned). The figures below illustrating my analysis were presented in a previous article (Maternal urinary fluoride/IQ study – an update). These results were close to those reported by Bashash et al., (2017).

For Fig. 2:

My comment was – “Yes, a “statistically significant” relationship (p = 0.002) but it explains only 3.3% of the variation in GCI (R-squared = 0.033).”

For Fig 3A:

My comment was – “Again, “statistically significant” (p = 0.006) but explaining only 3.6% of the variation in IQ (R-squared = 0.0357).”

So I in no way disagreed with the study’s conclusions quoted by Mary that:

” higher prenatal fluoride exposure, in the general range of exposures reported for other general population samples of pregnant women and nonpregnant adults, was associated with lower scores on tests of cognitive function in the offspring at age 4 and 6–12 y.”

I agree completely with that conclusion as it is expressed. But what Mary, Paul Connett and all other anti-fluoride activists using this study ignore is the real relevance of this reported association. The fact that it explains only about 3% of the IQ variance. I discussed this in the section “The small amount of variance explained“ in my article.

This is a key issue which should have been clear to any reader or objective attendee of Paul Connett’s meeting where the following slide was presented:

Just look at that scatter. It is clear that the best-fit line explains very little of it.  And the 95% confidence interval for that line (the shaded area) does not represent the data as a whole. The comments on the statistical significance and confidence intervals regarding to the best-fit line do not apply to the data as a whole.

Finally, yes I did write (as Mary quotes) in my introductory summary that “the study has a high degree of uncertainty.” Perhaps I should have been more careful – but my article certainly makes clear that I am referring to the data as a whole – not to the best fit line that Connett and Mary concentrate on. The regression analyses indicate the uncertainty in that data by the low amount of IQ variance explained (the R squared values) and the standard error of the estimate (about 12.9 and 9.9 IQ points for Fig 2 and  Fig 3A respectively).

The elephant in the room – unexplained variance

Despite being glaringly obvious in the scatter, this is completely ignored by Mary, Paul Connett and other anti-fluoride activists using this study. Yet it is important for two reasons:

• It brings into question the validity of the reported statistically significant association
• It should not be ignored when attempting to apply these findings to other situations like CWF in New Zealand and the USA.

Paul Connett actually acknowledged (in a comment on his slides) I was correct about the association explaining such small amount of the variance but argued:

• Other factors will be “essentially random with respect to F exposure,” and
• The observed relationship will not be changed by the inclusion of these other factors.

I explained in my article Paul Connett’s misrepresentation of maternal F exposure study debunked how both these assumptions were wrong. In particular, using as one example the ADHD-fluoridation study I have discussed elsewhere (see Perrott, 2017). I hope Mary will refer to my article and discussion in her response to this post.

While ignoring the elephant in the room – the high degree of scattering, Mary and others have limited their consideration to the statistical significance and confidence intervals of the reported association – the association which, despite being statistically significant, explains only 3% of the variation (obvious from the slide above.

For example, Mary quotes from the abstract of the Bashash et al., (2017) paper:

“In multivariate models we found that an increase in maternal urine fluoride of 0.5mg/L (approximately the IQR) predicted 3.15 (95% CI: −5.42, −0.87) and 2.50 (95% CI −4.12, −0.59) lower offspring GCI and IQ scores, respectively.”

I certainly agree with this statement – but please note it refers only to the model they derived, not the data as a whole. Specifically, it applies to the best-fit lines shown in Fig 2 and Fig 3A as illustrated above. The figures in this quote relate to the coefficient, or slope, of the best fit line.

Recalculating from 0.5 mg/L to 1 mg/L this simply says the 95% of the coefficient values, or slopes, of the best fit lines resulting from different resampling should be in the range  -10.84 to -1.74 CGI (Fig 2) and -8.24 to 1.18 IQ (Fig 3A).

[Note – these are close to the CIs produced in my regression analyses described above – an exact correspondence was not expected because digital extraction of data from an image is never perfect and a simple univariate model was used]

The cited CI figures relate only to the coefficient – not the data as a whole. And, yes, the low p-value indicates the chance of the coefficient, or slope, of the best-fit line being zero is extremely remote. The best fit line is highly significant, statistically. But it is wrong to say the same thing about its representation of the data as a whole.

This best-fit line explains only 3% of the variance in IQ – and a simple glance at the figures shows the cited confidence intervals for that line simply do not apply to the data as a whole.

The misrepresentation

That brings us back to the problem of misrepresentation. We should draw any conclusions about the relevance of the data in the Bashash et al., (2017) study from the data as a whole – not just from the small fraction with an IQ variance explained by the fitted line.

Paul Connett claimed:

“The effect size is very large (decrease by 5-6 IQ points per 1 mg/L increase in urine F) and is highly statistically significant.”

But this would only be true if the model used (the best-fit line) truly represented all the data. A simple glance at Fig 2 in the slide above shows that any prediction from that data with such a large scatter is not going to be “highly statistically significant.” Instead of relying on the CIs for the coefficient or slope of the line, Connett should have paid attention to the standard error for estimates from the data as a whole given in the Regression statistics of the Summary output. – For Fig. 2, this is 12.9 IQ points. This would have produced an estimate of “5-6 ± 36 IQ points which is not statistically significantly different to zero IQ points,”  as I described in my article

Confusion over confidence intervals

Statistical analyses can be very confusing, even (or especially) to the partially initiated. We should be aware of the specific data referred to when we cite confidence intervals (CIs).

For example, Mary refers to the CI values for the coefficients, or slopes, of the best fit lines.

Figs 2 and 3A in the Bashash et al., (2017) paper include confidence intervals (shaded areas) for the best fit lines (these take into account the CIs of the constants as well as the CIs of the coefficients). That confidence interval describes the region of 95% probability for where the best-fit line will be.

Neither of those confidence intervals applies to the data as a whole as a simple glance at Figs 2 and 3A will show. In contrast, the “prediction interval” I referred to in my article, does. This is based on the standard error of the estimate listed in the Regression statistics. Dr. Gerard Verschuuren demonstrated this in this figure from his video presentation.

Mary is perfectly correct to claim “it is the average effect on the population that is of interest” – but that is only half the story as we are also interested in the likely accuracy of that prediction. The degree of scatter in the data is also relevant because it indicates how useful this average is to any prediction we make.

Given the model described by Bashash et al., (2017) explained only 3% of the IQ variance, while the standard error of the estimate was relatively large, it is misleading to suggest any “effect size” predicted by that model would be “highly significant” as this ignores the true variability in the reported data. When this is considered the effect size (and 95% CIs) is actually “5-6 ± 36 IQ points which is not statistically significantly different to zero IQ points,”

Remaining issues

I will leave these for now as they belong more to a critique of the paper itself (all published papers can be critiqued) rather than the misrepresentation of the paper by Mary Byrne and Paul Connett. Mary can always raise them again if she wishes.

So, to conclude, Mary Byrne is correct to say that the model derived by Bashash et al., (2017) predicts that an increase of “fluoride level in urine of 1 mg/L could result in a loss of 5-6 IQ points” – on average. But she is wrong to say this prediction is relevant to New Zealand, or anywhere else, because when we consider the data as a whole that loss is “5-6 ± 36 IQ points.”

I look forward to Mary’s response.

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Anti-fluoride group coordinator responds to my article

Image credit: Debate. The science of communication.

My recent article Paul Connett’s misrepresentation of maternal F exposure study debunked got some online feedback and criticism from anti-fluoride activists. Mary Byrne, National coordinator Fluoride Free New Zealand, wrote a response and requested it is published on SciBlogs “in the interests of putting the record straight and providing balance.”

I welcome her response and have posted it here. Hopefully, this will satisfy her right of reply and help to develop some respectful, good faith, scientific exchange on the issue.

I will respond to Mary’s article within a few days.

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Perrott wrong. New US Government study does find large, statistically significant, lowering of IQ in children prenatally exposed to fluoride

By Mary Byrne, National coordinator Fluoride Free New Zealand.

While the New Zealand Ministry of Health remains silent on a landmark, multi-million-dollar, US Government funded study (Bashash et al), and the Minister of Health continues to claim safety based on out-dated advice, fluoride promoter Ken Perrott has sought to discredit the study via his blog posts and tweets.

Perrott claims that the results were not statistically significant but his analysis is incorrect.

The conclusion by the authors of this study, which was published in the top environmental health journal, Environmental Health Perspectives, was:

“In this study, higher prenatal fluoride exposure, in the general range of exposures reported for other general population samples of pregnant women and nonpregnant adults, was associated with lower scores on tests of cognitive function in the offspring at age 4 and 6–12 y.”

Perrott states the study has “a high degree of uncertainty”. But this contrasts with the

statistical analysis and conclusion of the team of distinguished neurotoxicity researchers from Harvard, the University of Toronto, Michigan and McGill. These researchers have written over 50 papers on similar studies of other environmental toxics like lead and mercury.

RESULTS: In multivariate models we found that an increase in maternal urine fluoride of 0.5 mg/L (approximately the IQR) predicted 3.15 (95% CI: −5.42, −0.87) and 2.50 (95% CI −4.12, −0.59) lower offspring GCI and IQ scores, respectively.

The 95% CI is the 95% Confidence Interval which is a way of judging how likely the results of the study sample reflect the true value for the population. In this study, the 95% CIs show the results are highly statistically significant. They give a p-value of 0.01 which means if the study were repeated 100 times with different samples of women only once could such a large effect be due to chance.

Perrott comes to his wrong conclusion because he has confused Confidence Intervals with Prediction Intervals and improperly used Prediction Intervals to judge the confidence in the results. A Prediction Interval is used to judge the confidence one has in predicting an effect on a single person, while a Confidence Interval is the proper measure to judge an effect on a population. In epidemiological studies, it is the average effect on the population that is of interest, not how accurately you can predict what will happen to a single person.

Despite the authors controlling for numerous confounders, Perrott claimed they did not do a very good job and had inadequately investigated gestational age and birth weight.

Once again Perrott makes a fundamental mistake when he says that the “gestational period < 39 weeks or > 39 weeks was inadequate” and “The cutoff point for birth weight (3.5 kg) was also too high.”

Perrott apparently did not understand the Bashash paper and mistook what was reported in Table 2 with how these covariates were actually treated in the regression models. The text of the paper plainly states:

“All models were adjusted for gestational age at birth (in weeks), birthweight (kilograms)”

Thus, each of these two variables were treated as continuous variables, not dichotomized into just two levels. Perrott’s criticism is baseless and reveals his misunderstanding of the Bashash paper.

Perrott states that the results are not relevant to countries with artificial fluoridation because it was done in Mexico where there is endemic fluorosis. But Perrott is wrong. The study was in Mexico City where there is no endemic fluorosis. Furthermore, the women’s fluoride exposures during pregnancy were in the same range as found in countries with artificial fluoridation such as New Zealand.

The study reports that for every 0.5 mg/L increase of fluoride in the urine of the mothers there was a statistically significant decrease in average IQ of the children of about 3 IQ points. It is therefore correct to say that a fluoride level in urine of 1 mg/L could result in a loss of 5 – 6 IQ points. This is particularly relevant to the New Zealand situation where fluoridation is carried out at 0.7 mg/L to 1 mg/L and fluoride urine levels have been found to be in this range².

There is no excuse for Health Minister, David Clark, to continue to bury his head in the sand. This level of science demands that the precautionary principle be invoked and fluoridation suspended immediately.

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Paul Connett’s misrepresentation of maternal F exposure study debunked

Title slide for Paul Connett’s presentation to parliament

Anti-fluoride campaigners are misrepresenting a recent Mexican study claiming its findings should cause governments around the world to abandon community water fluoridation (CWF). Their claims are unwarranted because the study has a high degree of uncertainty. Activists are misrepresenting the accuracy of the studies findings. Because Mexico has areas of endemic fluorosis the study itself is not relevant to CWF.

Misrepresentation of the Mexican study was a central argument used by US anti-fluoride activist Paul Connett in his recent New Zealand speaking tour. This is shown in the Powerpoint presentation he prepared for his meeting at parliament buildings last month (see Anti-fluoride activist commits “Death by PowerPoint”).

It may have not been used in the end as only 3 MPs turned up. But, given his status in the anti-fluoride movement, this presentation will present the current strongest arguments against CWF. It is therefore worth critiquing his presentation whether it was given or not.

In this article, I will concentrate on Paul’s presentation of the Mexican study and may deal with other arguments used in the presentation in later articles. The paper reporting the study is:

Bashash, M., Thomas, D., Hu, H., Martinez-mier, E. A., Sanchez, B. N., Basu, N., … Hernández-avila, M. (2016). Prenatal Fluoride Exposure and Cognitive Outcomes in Children at 4 and 6 – 12 Years of Age in Mexico.Environmental Health Perspectives, 1, 1–12.

In Connett’s mind, the study’s results are so overwhelming we should immediately stop fluoridation throughout the world! This was the first and main argument he presented. His title slide and slide no. 10 introducing the study demonstrates the importance to him.

Slide No. 10 introducing Connett’s presentation of the Bashash et al (2017) study.

I have critiqued this study in previous articles – readers can find them at:

Fluoride, pregnancy and the IQ of offspring,
Maternal urinary fluoride/IQ study – an update,
Anti-fluoridation campaigners often use statistical significance to confirm bias and
Paul Connett “updates” NZ MPs about fluoride?

Paul is clearly aware of these articles because he included a note in his presentation about them. I am honoured (it is the only comment in the presentation) and pleased he has made an effort to engage with my critique.

This is what he says:

“Ken Perrott and those who follow him will claim that the wide degree of scatter in the data means the findings of this study are unreliable.  That is an incorrect interpretation of this graph and the study.  The effect size is very large (decrease by 5-6 IQ points per 1 mg/L increase in urine F) and is highly statistically significant.  The fact that urine F can only explain a small amount of the variation of IQ does not invalidate the finding.  Rather, it is a reflection that there are many other factors that affect IQ, most of which are essentially random with respect to F exposure.  For example, individual genetics plays a huge role in IQ (it explains 80% or more of variation in IQ), therefore it would not be possible for F to explain more than the small remaining portion of variation in IQ.  Most studies of other developmental neurotoxins like Pb and Hg find very similar low correlation coefficients, yet there is no debate that their findings are valid.”

This comment provides me with a basis for a more detailed discussion of his use of the study.

The small amount of variance explained

Connett acknowledges my point that the observed relationship with urinary fluoride can explain only a very small amount of the variation in IQ – only 3%. A bit hard to deny considering the high degree of scatter in the data which is obvious even in the slides Connett uses:

Slide 20 where Connett reproduces Fig 2 from the Bashash et al. paper.

But he claims that this:

“does not invalidate the finding. Rather, it is a reflection that there are many other factors that affect IQ, most of which are essentially random with respect to F exposure.”

Here he is, of course, referring to his own “finding” or conclusion – not the authors.

Notice his assumptions:

• Other factors will be “essentially random with respect to F exposure,” and
• The observed relationship will not be changed by the inclusion of these other factors.

Those are huge assumptions. And they are wrong.

Here is a relevant example illustrating the danger of such assumptions – the association between ADHD prevalence and extent of fluoridation observed by Malin & Till (2015). Their association was able to explain between 22% and 31% of the variance in ADHD, depending on the specific data used. Far more than the 3% for the Bashash et al., (2017) study.

Yet, when other risk-modifying factors were included, in this case, mainly altitude, the significant association with fluoridation disappeared. A model including altitude, but not fluoridation, explained 46% of the variability in ADHD (see Perrott 2017 and a number of articles in this blog).

In this case, the incidence of fluoridation was correlated with altitude – fluoridation was simply acting as a proxy for altitude in the Malin & Till (2015) association. So much for Connett’s assurance that other factors “are essentially random with respect to F exposure.”

Other studies have found an association between symptoms of fluorosis and cognitive deficiencies. Choi et al., (2015), for example, reported an association of child cognitive deficits with severe dental fluorosis, but not with water F concentration. But there is a relationship between fluoride exposure and fluorosis prevalence – ie. fluorosis is not random with respect to F exposure. If the health effects resulting from fluorosis are the prime cause of the cognitive deficiency, the inclusion of fluorosis incidence in the multiple regression could produce a model where there is a statistically significant association with fluorosis but not with fluoride expose. That is, the urinary fluoride values could be simply acting as a proxy of fluorosis incidence.

A similar non-random association of premature births and low birth weight could occur because these problems do occur in areas of endemic fluorosis. These could be two of the health issues related to fluorosis but fluoride intake may not be the prime cause (see Premature births a factor in cognitive deficits observed in areas of endemic fluorosis?).

Connett is completely wrong to assume that other risk-modifying factors not considered in the Bashash study would necessarily be random with respect to fluoride exposure. And he is wrong to assume that inclusion of these factors would not change the association of child IQ with mothers’ urinary fluoride reported in the paper.

Notably, the Bashash et al (2017)study did not include any measure of fluorosis as a risk-modifying factor – despite the fact that Mexico has areas of endemic fluorosis. I believe its consideration of gestation period <39 weeks or >39 weeks was inadequate (the normal average period is 40 weeks). The cutoff point for birth weight (3.5 kg) was also high.

The size of the IQ effect

We only have the data in the Bashash et al., (2017) study to go with here and the associations they report are valid for that data. But what about the calculations Connett makes from the reported association.

For example, Connett declares:

” The effect size is very large (decrease by 5-6 IQ points per 1 mg/L increase in urine F) and is highly statistically significant.”

Let’s test this claim – using the association represented in Fig 2 from Bashash, which is the figure Connett and other anti-fluoride activists are using (his slide 20 above).

Firstly, we need to calculate prediction intervals from the data (see Confidence and prediction intervals for forecasted values). The shaded region in the figure used by Connett (Fig 2 in Bashash et al., 2017) represents the confidence interval – the region where there is a 95% probability that a best-fit line for the data lies. The region for the prediction intervals is much larger and Connett may be confused because he has interpreted the confidence interval wrongly. Yet, the prediction intervals are the important measure when considering the effect size.

Here are my graphs for the confidence interval and the prediction interval using data I digitally extracted from the paper (see Maternal urinary fluoride/IQ study – an update).

Let’s consider the predicted values of “child IQ” for urinary F concentrations of 0.5 and 1.5 mg/L.

Urine F (mg/L)	Predicted value	Lower	Higher
0.5	99.8	74.4	125.2
1.5	93.0	67.5	118.4

The prediction intervals are very large. This means the real value for “child IQ” at a urine F value of 0.5 mg/L has a 95% probability of being in the range 74.4 – 125.2. The corresponding range for a urine F concentration of 1.5 mg/L is 67.5 – 118.4. When Connett claims that an increase of 1 mg/mL in mother’s urinary F produces a drop of 5 – 6 IQ points he actually means a drop of 5 – 6 ± 26 IQ points which is not statistically significantly different to zero.

The best-fit line for the data may be statistically significant – but Connett is wrong to say this about his predicted effect of urinary F on child IQ. In fact, over the whole range of urinary F measured there is a 95% probability that IQ remains at 100.

Connett’s claim of a “highly statistically significant” effect size is completely false. If he had simply and objectively looked at the scatter in the data points he would not have made that mistake.

Comparing maternal urinary F levels to other countries

Connett makes an issue of the similarity of maternal urinary F levels found in this Mexcian study to levels found elsewhere. One is tempted to say – so what? After all, I showed above that his claim of a “highly statistically significant” drop in child IQ with increases in maternal urinary F is completely wrong.

He does compare the urine F levels reported by Bashash et al., (2017) with some New Zealand data (Brough et al., 2015) and finds them to be very similar. Interestingly, Brough et al., (2015) reported their urinary F values as indicating fluoride intakes were inadequate for the women concerned. They certainly did not indicate toxicity.

The comparison does highlight for me one of the inadequacies in the Bashash (2017) paper – the inadequate measurements of urinary F. Whereas Borough et al., (2015) used the recommended 24-hr urine collection technique, the data used by Bashash et al (2017) relied on spot rather than 24 hr measurements. These spot measurements were only made once or twice during the pregnancy of these women.

Yes, these were the only F exposure measurements Bashash et al., (2017) had to work with but they are far from adequate.

Conclusions

Paul Connett, as a leader of the anti-fluoridation movement, is completely wrong about the Bashash et al., (2017) study. It will not lead to the end of community water fluoridation throughout the world – nor should it.

He has attempted to ignore, or downplay, the high scatter in the data and the low explanatory power of the relationship between children’s IQ and maternal F exposure found in the study (only 3%). His denial that this relationship may disappear when other more important risk-modifying factors are included is also wrong – as other examples clearly show.

Connett’s presentation of a size effect (5-6 IQ points with a 1 mg/L increase in F exposure) as “highly statistically significant” is also completely wrong. In fact, this size effect is more like 5 – 6 ± 26 IQ points which is not significantly different to zero.

The misrepresentation of this study by Paul Connett and other anti-fluoridation activists demonstrates, once again, that their claims should never be accepted uncritically. This is just one more example of the way their ideological and commercial interests drive them to misrepresent scientific finding.

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“Other ways of knowing” purpose?

A recent panel discussion in Mexico debated the question “Does the universe have a purpose?” The speakers for the affirmative were Rabbi David Wolpe, William Lane Craig and Douglas Geivett. And for the negative Matt Ridley, Michael Shermer and Richard Dawkins.

I don’t think the discussion was very good. Contributions were short and the original video is in Spanish. It’s also full of hoopla. Reminds we of an international scientific congress I attended in Mexico some time ago. All the official meetings involved many young women as decoration. And the Mexicans are certainly a very musical people. Music was everywhere.

However, I have included a video below of the short contribution made by Richard Dawkins in this discussion. It gives an idea of the issues discussed:

Vodpod videos no longer available.Prof.Richard Dawkins destroys Dr.William Lane C…, posted with vodpod