Author confirms anti-fluoridation activist misrepresentation of her work

Anti-fluoride activists like Paul Connett often distort scientific findings and misrepresent studies. Authors should not give him a free pass and should expose such misinformation about their findings. Image credit: World Congress for freedom of scientific research

Paul Connett, director of the Fluoride Action Network (FAN),  claims “You only have to read four studies…” to come to the conclusion that community water fluoridation (CWF) is bad for your health. One of the studies he refers to is that of Riddell et al (2019) and Connett claimed that this study found “a staggering 284% increase in the prevalence of ADHD among children in fluoridated communities in Canada compared to non-fluoridated ones.”

I commented on this claim in my article Anti-fluoridation propaganda now relies on only four studies. 3: Riddell et al (2019) saying:

“This is just so wrong – Connett has misinterpreted the findings in this paper and completely covered up the fact that the results were dependent on age. He may well be “staggered” but he has made a bad mistake.”

Now, the senior author of this paper has confirmed that Connett was mistaken. She has confirmed that Connett is misrepresenting her work.

I emailed Julia Riddell to check if the Table 4 in her paper was mislabeled because some of the results were from linear regression analyses rather than logical regression analyses (see Anti-fluoridation propaganda now relies on only four studies. 3: Riddell et al (2019)). I also took the opportunity to ask her about Connett’s claim.

In her response she writes:

“I agree with you that the statement “a staggering 284% increase in the prevalence of ADHD among children in fluoridated communities in Canada” is a misinterpretation of our results, . . “

Riddell gives two reasons for saying this:

“First, a percentage should only be used to calculate values that are part of a whole, and thus a percentage is not an appropriate descriptive statistic to discuss these results. Further, and more importantly, I believe this interpretation of the odds ratio is incorrect. Odds is not probability nor is it likelihood of a ADHD diagnosis. Instead, odds = probability(ADHD dx) / probability(no ADHD dx).

In order to calculate a percent increase in the prevalence of ADHD, we would need to know the base rate probability of an ADHD diagnosis in the absence of water fluoridation (that is, what is the prevalence of ADHD ONLY in non-fluoridated communities).”

She writes: “To my knowledge, there has never been a national prevalence rate of ADHD calculated only for non-fluoridated regions, which is why we didn’t interpret the results in this way in our paper.” However, she provides an estimate using a postulated ADHD base rate of 0.08 (8% prevalence). According to her the Odds Ratios she reported in her paper would mean fluoridation would increase ADHD prevalence from 8% to 10%.

This is very different from Paul Connett’s claim. He should withdraw his claim and apologise for his mistake.

Two questions

Paul Connett’s little fiasco raises two questions for me:

1: Will Paul Connett now withdraw his claim and apologise for his mistake/misrepresentation?

I guess the answer is “when Hell freezes over.” He is not known for such apologies – neither is the anti-fluoride movement he leads. It continually misrepresents scientific findings and has never, in my experience, apologised for that misrepresentation when it has been exposed.

In fact, the anti-fluoride movement’s main propaganda thrust relies on citing any scientific work it can present, or misrepresent, as harmful to the case for community water fluoridation. This way it pretends to have the backing of science while relying on the unwillingness or inability of policymakers they bombard with the propaganda to factually check out these claims.

2: Why are researchers not more proactive in countering misrepresentation of their findings like this?

Yes, I know, researchers and especially their institutions do not like to enter into public debates with activists. There is some logic in this – after all, there is the old adage that it is not worth wrestling a pig because the pig enjoys it and both sides end up covered in dirt.

But this particular case is an example of misrepresentation which is being promoted to health policymakers who mainly do not have the scientific skills to check out the claims. It’s not a matter of entering into an unsavoury fight – but of correctly informing these policymakers when such misrepresentation occurs.

The other concern I have is that some anti-fluoride activists, including some well-known members (sometimes paid members) of Paul Connett’s FAN, have developed links with researchers and appear to be influencing publications through the journal peer-review process. In my article Anti-fluoridation propaganda now relies on only four studies. 6: Incestuous relationship of these studies I show how incestuous the publication and peer review process is for some of the scientific papers currently being promoted by anti-fluoride activists. Perhaps this is not too uncommon in science publication (many scientists complain about this sort of thing happening). But the participation of FAN members in the journal peer-review process is worrying as it suggests a relationship which FAN can use to get information about upcoming papers and prepared their propaganda and misinformation claims accordingly.

On the one hand, this may mean authors are hesitant to criticism FAN members and their claims because this could rebound on them during the publication process. On the other hand, it surely means that these people should be treated as valid science commentators and therefore open to challenge by researcher without the stigma of entering into an activist debate.

Similar articles

Anti-fluoridation propaganda now relies on only four studies. 3: Riddell et al (2019)

Connett promotes Riddell et al (2019) as one of the only four studies one needs to read about fluoridation. But he misunderstands and misrepresents the findings of this study. Image credit: Fluoride Action Network – with my addition.

For earlier articles in this series see:

Part 1: Anti-fluoridation propaganda now relies on only four studies. 1: Bashash et al (2018).

Part 2: Anti-fluoridation propaganda now relies on only four studies. 2: Green et al (2019).

Paul Connett, director of the Fluoride Action Network (FAN), now claims “You only have to read four studies…” to come to the conclusion that community water fluoridation (CWF) is bad for your health. As I said in the first article in this series that is simply bad science. One should not ignore all the other relevant studies – and anyway, these four studies do not say what Connett claims.

In this article, I discuss the third study Connett recommends. It’s citation 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.

Riddell et al (2019)

Connett switches his attention from IQ to ADHD saying:

 “The third came in 2019 and found a staggering 284% increase in the prevalence of ADHD among children in fluoridated communities in Canada compared to non-fluoridated ones.”

This is just so wrong – Connett has misinterpreted the findings in this paper and completely covered up the fact that the results were dependent on age. He may well be “staggered” but he has made a bad mistake.

The results reported in Riddell et al (2019) are a mixed bag and somewhat confused – see Table 4. They could not find any significant effect of urinary F (UF) on the diagnosis of ADHD – a result that disappointed them as they understood UF to be the most reliable measure of fluoride exposure. They did find a significant effect of CWF status on ADHD diagnosis (increase odds of diagnosis) but only for older children. There was also a significant effect for water-F.

Table 4: Results of logical regression of ADHD diagnosis against measures of fluoride exposure obtained by Riddell et al (2019). Red tringel indicates significant effect.

But Connett confused himself when attempting to interpret Riddell’s results for the effect of CWF – probably because he did not understand the difference between linear and logical regressions. Riddell et al (2019) did not use data for ADHD prevalence (as Connett implied) so could not produce a relationship of prevalence to CWF. Their data was binary – ADHD diagnosis vs no ADHD diagnosis – and they determined the chance of an ADHD diagnosis in fluoridated compared with unfluoridated areas. Here is how they describe that result:

“Specifically, at the 75th percentile of age (14 years old), the predicted odds of an ADHD diagnosis was 2.8 times greater among youth in a fluoridated region compared with youth in a non-fluoridated region (aOR=2.84, 95% CI: 1.40, 5.76, p < .01), whereas among youth at the 25th percentile of age (9 years old), the predicted odds of an ADHD diagnosis was similar across CWF status (aOR=0.91, 95% CI: 0.41, 1.99, p=.81; Table 4).”

So there was no “staggering 284% increase in the prevalence of ADHD among children in fluoridated communities .  .” Just that the chance, or predicted odds, of an ADHD diagnosis was 2.84 times greater for 14-year-old youth in fluoridated areas (but the same for 9-year-olds). The odds ratio of 2.84 is still relatively small (see Rules of thumb on magnitudes of effect sizes). And Connett ignored the fact this result was age-specific.

They also report results for the hyperactivity/inattention subscale score from the Strengths and Difficulties Questionnaire (SDQ h/i). Again, no significant effect of UF but significant effects of CWF and water-F for older children. I won’t comment on this further because the data reported in the paper is confused – probably because of a mistake in the paper’s Table 4. I have emailed Julia Riddell about this problem but not yet had a reply.

Connett’s claim of a “284% increase in the prevalence of ADHD” due to fluoridation is simply wrong and demonstrated he did not understand the statistical analysis used in this paper. 

Tomorrow I will discuss the fourth study Connett now relies on – Till et al (2020) – see Anti-fluoridation propaganda now relies on only four studies. 4: Till et al (2020).

See also:

• ADHD and fluoride – wishful thinking supported by statistical manipulation?
• Statistical manipulation to get publishable results
• Anti-fluoridationists put faith in new “strong” studies to provide evidence missing in draft NTP review
• What are the recent fluoride-IQ studies really saying about community water fluoridation?

Similar articles

What are the recent fluoride-IQ studies really saying about community water fluoridation?

Scaremongering graphic currently being promoted by Declan Waugh who is well known for misrepresenting the fluoride science

This graphic is typical of current anti-fluoride propaganda. It is scare-mongering, in that it is aimed at undermining community water fluoridation (CWF) which is accepted by health and scientific authorities as safe and effective. It relies on citations of recent research to give an impression of scientific credibility – but it misrepresents that research.

In fact, this research has produced confusing and contradictory results based on very weak relationships. Instead of the cherry-picking commonly indulged in by anti-vaccination and anti-fluoridation groups like this, all the findings in these studies must be considered. In this article, I have attempted to graphically present all these findings in one place. This makes clear how weak the evidence these activist groups rely on is and why it does not provide a basis for reviewing the current acceptance of CWF.

I have given below all the reported findings from the recent research (including the citations given by Declan Waugh in the above graphic). There is a lot here (I have not cherry-picked as the anti-fluoride activists do) so I present the findings graphically to provide a complete overview without the boring job of trying to understand  detailed text. My apologies for the length of this article.

NOTE: I recommend readers refer to the cited papers for more details on methodology and definitions of the cognitive measures and F-exposure measures.

Does fluoridation influence IQ?

The answer provided by these modern studies is clearly no. Remember, these studies use data from areas where CWF is used or where drinking water concentrations are similar. They are quite different from the studies (previously relied on by anti-fluoridation activists) from areas of endemic fluorosis where fluoride intake is much higher and where many health problems result.

All the comparisons from fluoridated and unfluoridated areas in these studies are presented in Figure 1 below. The bars represent the standard deviations for the data sets and the data points are the means. A * indicates differences are statistically significant.

Figure 1:  Comparison of IQ results in fluoridated and non0-fluoridated areas

The only statistically significant differences are for verbal IQ (VIQ) of 3-4 year-olds breastfed as babies (where the VIQ of children in fluoridated areas is higher) and for performance IQ (PIQ) of the same group (where the PIQ of children in fluoridated areas is lower). Till et al (2020) had to dig deep, use multiple measures of the cognitive score and subdivide the children into groups, to find an occasional difference. And these differences are contradictory.

I discussed the Till et al (2020) study which reported these occasional differences in Anti-fluoride propagandists appear not to read the articles they promote.

What about the relationships between IQ and measures of F intake?

Anti-fluoride propagandists ignore the data presented in Figure 1 above (and reported in the papers involved) and instead rely on cherry-picked relationships between measures of cognitive ability and various measures of fluoride exposure. Yes, some of these relationships, but only a small proportion, are statistically significant. But, importantly, none of these explain more than a few percents of the variation of the cognitive measure used.

Figure 2 below displays all the results from all the recent studies where linear regressions were used. The coefficient represents the size of the effect (eg., the change in IQ for every 1 mg/L increase of F exposure measure like drinking water F or maternal urinary F) and the bars represent the 95% confidence levels. The statistically significant (p<0.05) relationships are represented by red points while the green data points represent nonsignificant results.

Figure 2: Relationships of cognitive measures with exposure to fluoride obtained by linear regression analyses

Footnote: UF – concurrent urinary fluoride of the child. UFsg – UF adjusted using the specific gravity of urine. MUF – maternal prenatal urinary fluoride. MUFcr MUF – adjusted using urinary creatinine concentration. MUFsg – MUF adjusted using urine specific gravity. FSIQ – Full-Scale IQ. VIQ – Verbal IQ. PIQ – performance IQ. MDI – Mental development index.

Figure 3 below displays the results obtained by Barberio et al (2017) using logical regression of learning disabilities in children aged 3-12 years on urinary fluoride (UF), specific gravity adjusted urinary fluoride (UFsg), and creatinine adjusted urinary fluoride (UFcr). The data used was from two cycles of the Canadian Health Measures Survey (CHMS).

Findings for logical regression of ADHD and ADD on urinary fluoride are also included.

Figure 3: Relationships of cognitive measures with exposure to fluoride obtained by logical regression analyses

There are a lot of reported relationships in these two figures but only a few are statistically significant. Even these are contradictory – Thomas (2014) and Santa-Marina (2019) found positive coefficients while Bashash et al (2017), Thomas (2018), Green et al (2019) and Till et al (2020) reported some negative relationships. Barberio et al (2017) found a positive relationship for the data from combined CHMS cycles but this disappeared when UFsg or UFcr was used. Most of the reported relationships are not statistically significant.

Moving from nonsignificant to significant by adjusting urinary-F figures

This is illustrated by the evolution of the way the results are presented for the Thomas (2014) study which is related to the Bashash et al (2017) study. In this thesis and early conference reports (Thomas et al 2013 & Thomas et al 2014), She did not find any statistically significant relationships of child IQ with maternal urinary F (MUF) or maternal blood plasma F. But she did report a statistically significant relationship with MUFcr in her last conference paper (Thomas et al 2018).

So what happened?

There appears to be a change in the actual mother-child pairs used as indicated by the numbers and this sort of data selection can easily push a nonsignificant relationship into significance – especially when the relationship is so weak (see A conference paper on the maternal prenatal urinary fluoride/child IQ study has problems).

The other factor is that in the 2018 conference paper she has adjusted the MUF figures using creatinine concentration. Use of individual urinary fluoride measures, especially spot samples rather than a 24-hr collection, is a problem and is not a good measure of F exposure. Adjustment of urinary F using specific gravity or creatinine concentration is often used to improve the measure but this is problematic because creatinine concentration is influenced by a range of other factors.  The adjusted MUF figures may actually be acting as a proxy for one of these other factors. This is why Barr et al (2005) recommended that:

“ For multiple regression analysis of population groups, we recommend that the analyte concentration (unadjusted for creatinine) should be included in the analysis with urinary creatinine added as a separate independent variable. This approach allows the urinary analyte concentration to be appropriately adjusted for urinary creatinine and the statistical significance of other variables in the model to be independent of effects of creatinine concentration.”

This is not done by any of the authors of these recent papers where urinary fluoride was used.

Thomas (2014) also reported a positive relationship of concurrent child urinary F (UF) with a cognitive measure, but not for girls. This seems to have been ignored in later reports – and by Bashash et al (2017) which used the same data but only reported the non-significant result for all children.

Till et al (2020) found that only relationships with PIQ were statistically significant. It is not clear why this happened considering no significant relationships were found for FSIQ or VIQ. It’s interesting that Till et al (2019) initially did not report the PIQ results and instead relied on a significant relationship of FSIQ with water F in children formula-fed as babies. Maybe the PIQ measurement is considered unreliable in practice. This finding was also heavily promoted by ant-fluoride campaigners – despite the fact that adjustment for other factors made this relationship nonsignificant (see Anti-fluoride propagandists appear not to read the articles they promote).

Most anti-fluoride campaigners have stuck with the initial FSIQ relationship – although a few who may have read the paper are now cherry-picking the PIQ relationships and ignoring the others.

What about fluoride and ADHD?

Three of these recent studies used linear regression when considering ADHD – but those of Malin & Till (2015) (claimed to be the first study to suggest an effect of fluoridation on ADHD) and Perrott (2018) are important. Not because one of the studies is mine – but because they illustrate a basic problem with correlation studies.

Even when multiple regression is used to adjust for covariants or other possible risk-modifying factors the investigation may miss an important risk-modifying factor. Not only does correlation not prove causation – the “significant” relationships themselves may be false when important risk-modifying factors are not included in the multiple regressions.

This happened with the Malin & Till (2015) study which reported statistically significant relationships of the extent of fluoridation in US states with ADHD prevalence. However, when the mean state elevation was included in the multiple regression of exactly the same data by Perrott (2018) the relationship with fluoridation extent disappeared (this had a p-value of 0.269 whereas those for Malin & Till 2015 were <o.o5). See Figure 4 below.

Figure 4: The effect of including other important risk-modifying factors on reported significant relationships

Figure 5 below shows the data reported by Bashash et al (2018) for the linear regression of a range of ADHD symptoms against urinary fluoride (UFcr).

Figure 5: Relationships of ADHD symptoms with exposure to fluoride obtained by linear regression analyses

The relationships were statistically significant for only four of the ten symptoms considered. Those relationships were very weak, explaining only a few per cent of the variance in ADHD prevalence (see Fluoridation and ADHD: A new round of statistical straw clutching).

The logistical regression results reported by Riddell et al (2019) for ADHD diagnosis and SDQ subscale score Urinary fluoride (UFcr) are given in Figure 6 below.

Figure 6: Relationships of ADHD diagnoses  with exposure to fluoride obtained by logistical regression analyses

I discussed Riddell et al (2019) in my article ADHD and fluoride – wishful thinking supported by statistical manipulation?

This is another case where authors found unpromising results (no significant relationship for UFsg for example) and searched for other measures. It is also interesting that the significant relationships for water F and CWF status disappeared for younger children when age separation was used. The large confidence intervals in most cases indicate a large scatter in the data and very weak relationships.

I should also mention here the nonsignificant relationships reported by Barberio et al (2017) for ADHD and ADD (see Figure 3 above). These just underline how significant relationships are not common in these recent studies when looked at overall.

Update: Fluoride and sleep disturbances

Strictly, sleep disturbances don’t come under the classification of cognitive effects but a recent paper on fluoride and sleep disturbances is being promoted by anti-fluoride campaigners and should, therefore, be included here. For the sake of completeness.

I discussed the paper, Malin et al (2019), in my article Sleep disorders and fluoride: dredging data to confirm a bias. All the findings reported in that paper, and the supplementary files, are presented in Figure 7 below.

The authors report relationships of a range of sleep disorders against two measures of fluoride exposure – blood plasma-F and tap water-F. None of the relationships with blood plasma were significant (most had a p-value of 1.0). I discussed these in Sleep disorders and fluoride: dredging data to confirm a bias. and made the point that that bedtime and waketime were likely to be related to residence and the tap water F was simply acting as a proxy for regional location.

But again we see the use of a large number of measures for a “disorder’ and very few statistically significant relationships which are probably better explained by other factors than fluoride.

Conclusion

Considering all the findings together of the recent studies relevant to community water fluoridation and cognitive factors shows the results are weak, conflicting, and contradictory. This is probably not surprising considering the nature of the data (the studies were basically exploratory using existing databases – not designed experiments). Although adjustments were made for other possibly important factors this does not mean those really important ones (like the relationship of ADHD prevalence to elevation) were included. All the statistically significant relationships found were very weak – explaining a small proportion of the variance in the cognitive measure.

This is the sort of picture one might expect from exploratory studies using a large number of cognitive factors and measure of fluoride exposure. While these results may be useful in suggesting possible hypotheses to check in future better-designed experiments they are not sufficiently coherent to inform social health policy.

These recent studies do not provide sufficient evidence for revision of community water fluoridation policies because of possible effects on cognitive abilities. Anti-fluoride activists have only been able to use these studies in their scaremongering propaganda by cherry-picking results and ignoring the weakness of the relationships they cite.

References

Aggeborn, L., & Öhman, M. (2016). The Effects of Fluoride In The Drinking Water.

Barberio, A. M., Quiñonez, C., Hosein, F. S., & McLaren, L. (2017). Fluoride exposure and reported learning disability diagnosis among Canadian children: Implications for community water fluoridation. Can J Public Health, 108(3), 229.

Barr, D. B., Wilder, L. C., Caudill, S. P., Gonzalez, A. J., Needham, L. L., & Pirkle, J. L. (2005). Urinary creatinine concentrations in the U.S. population: Implications for urinary biologic monitoring measurements. Environmental Health Perspectives, 113(2), 192–200.

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. Enviromental Health Perspectives, 1, 1–12.

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.

Broadbent, J. M., Thomson, W. M., Ramrakha, S., Moffitt, T. E., Zeng, J., Page, L. A. F., & Poulton, R. (2015). Community water fluoridation and intelligence: Prospective study in New Zealand. American Journal of Public Health, 105(1), 72–76.

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.

Malin, A. J., & Till, C. (2015). Exposure to fluoridated water and attention deficit hyperactivity disorder prevalence among children and adolescents in the United States: an ecological association. Environmental Health, 14(1), 17.

Malin, A. J., Bose, S., Busgang, S. A., Gennings, C., Thorpy, M., Wright, R. O., … Arora, M. (2019). Fluoride exposure and sleep patterns among older adolescents in the United States : a cross-sectional study of NHANES 2015 – 2016. Environmental Health, 1–9. Retrieved from https://link.springer.com/article/10.1186/s12940-019-0546-7

Perrott, K. W. (2018). Fluoridation and attention deficit hyperactivity disorder a critique of Malin and Till (2015). British Dental Journal, 223(11), 819–822.

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.

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.

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.

Thomas, D., Hu, H., Basu, N., Sanchez, B., Bellinger, D., Schnaas, L., … Tellez-Rojo, M. M. (2013). A prospective study of prenatal exposure to fluoride and neurobehavior : preliminary analyses. Environmental Health Perspectives.

Thomas, D., Hu, H., Basu, N., Martinez-Mier, E. A., Sanchez, B., Bellinger, D., … Tellez-Rojo, M. M. (2014). Urinary Fluoride in Pregnant Women and Prenatal Fluoride Exposure and Mental Development Index ( MDI ) in 1-3 Year Old Infants from Mexico City, Mexico. Environmental Health Perspectives, 1(Icc), 2–3.

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.

Till, C., Green, R., Flora, R., Hornung, R., Martinez-Mier, E., Blazer, BFarmus, L., … Lanphear, B. (2019). Fluoride Exposure from Infant Formula and Child IQ in a Canadian Birth Cohort. Environmental Epidemiology, 3.

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

Similar articles

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:

“UF_SG [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:

“UF_SG 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:

• 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

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.

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.

Similar articles

 

 

Anti-fluoridation campaigners often use statistical significance to confirm bias

I was pleased to read this Nature article – Five ways to fix statistics – recently as it mirrors my concern at the way statistical analysis is sometimes used to justify or confirm a bias and not reveal a real causal relationship. Frankly these days I just get turned off by media reports of studies showing statistically significant relationships as evidence for or against the latest health or other fads.

As the Nature article says, statistical significance tests often amount “to uncertainty laundering:”

“Any study, no matter how poorly designed and conducted, can lead to statistical significance and thus a declaration of truth or falsity. NHST [null hypothesis significance testing] was supposed to protect researchers from over-interpreting noisy data. Now it has the opposite effect.”

No matter how good a relationship appears, or how significant the statistical analysis shows it to be, it is simply a relationship and may have no mechanistic or causal backing.  An example often used to illustrate this is the close relationship between the prevalence of autism and sales of organic produce.

Clearly statically significant but we don’t find those activists claiming autism is related to one thing or another ever citing this one. I am picking these activists may well have a bias towards organic produce.

Here are several examples I have discussed before which illustrates how “statistical significance” is sometimes used to confirm bias in fluoridation studies. I think these are very relevant as anti-fluoridation campaigners often cite statistical significance as if it is the final proof for their claims.

Ignoring relevant confounders

This is an easy trap for the biased researcher (and let’s face it, most of us are biased – it’s only human). Just ignore other confounders or risk-modifying factors that may be more important. Or ignore the fact that the risk-modifying factor one is interested in (in this case fluoride) may just be acting as a proxy for (and therefore is related to) something else which is more relevant.

This why all credible risk-modifying factors should be considered in correlation studies. They should be included in the statistical analyses.

It’s amazing how many researchers either ignore the possible risk-modifying factors besides their pet one – or pay lip-service to the problem by limiting their consideration to only a small range of such factors.

Examples of studies promoted by anti-fluoride campaigners where this is a problem include:

Peckham et al., (2015) hypothyroidism paper:

Peckham, S., Lowery, D., & Spencer, S. (2015). Are fluoride levels in drinking water associated with hypothyroidism prevalence in England? A large observational study of GP practice data and fluoride levels in drinking water. J Epidemiol Community Health, 1–6.

This has been widely condemned for a number of reasons – one of which is that iodine deficiency, a known factor in hypothyroidism, was not included in the statistical analysis.

(See Paper claiming water fluoridation linked to hypothyroidism slammed by experts and Anti-fluoride hypothyroidism paper slammed yet again).

The  Takahashi et al., (2001) cancer paper:

Takahashi, K., Akiniwa, K., & Narita, K. (2001). Regression Analysis of Cancer Rates and Water Fluoride in the USA based Incidence on IACR / IARC ( WHO ) Data ( 1978-1992 ). Journal of Epidemiology, 11(4), 170–179.

These authors reported an association between fluoridation and a range of cancers. Problem is, they did not consider any other risk-modifying factors. When some geographical parameters were included in the statistical analyses there were no statistically significant relationships of cancer with fluoridation.

(see Fluoridation and cancer).

The Malin & Till (2015) ADHD paper:

Malin, A. J., & Till, C. (2015). Exposure to fluoridated water and attention deficit hyperactivity disorder prevalence among children and adolescents in the United States: an ecological association. Environmental Health, 14.

This reported an association of ADHD prevalence with the extent of fluoridation in the US. Anti-fluoride campaigners have cited this paper a lot because it is the only study indicating any effect of fluoridation on cognitive ability. All other studies they rely on were from areas of endemic fluorosis where the natural levels of fluoride are higher than that used in community water fluoridation.

Malin & Till (2015) considered only household income as a possible risk-modifying factor. No consideration was given to residential elevation which other researchers had around the same time reported as associated with ADHD prevalence.

I repeated their statistical analysis but included residential elevation and a range of other risk-modifying factors. This showed there was no statically signficant association of ADHD with fluoridation when other risk-modifying factors, particularly elevation, were included. My critique of Malin and Till (20215) is now published:

Perrott, K. W. (2017). Fluoridation and attention deficit hyperactivity disorder – a critique of Malin and Till ( 2015 ). Br Dent J.

(See ADHD linked to elevation not fluoridation, ADHD link to fluoridation claim undermined again and Fluoridation not associated with ADHD – a myth put to rest).

Ignoring the lack of explanatory power

I think this is where the over-reliance on statistical significance, the p-value, can be really misleading. Researchers desperately wishing to confirm their bias will proudly claim  a statistically significant relationship, a p-value less than 0.05, etc., as if that is the final “proof.” These researchers will often hide the real meaning of their relationship by not making the actual data available or limiting their report of their statistical analysis to p-vlaues and, maybe, a mathematical relationship.

However, if the reported relationship actually explains only a small part of the observed variation in the data it may be meaningless. Concentration on such a relationship means that other more signficant risk-modifying factors which would explain more of the variation are ignored. Anyway, where a factor explains only a small part of the variation it is likely a more complete statistical analysis would show that its contribution was not actually statistically signficant.

Some examples:

The prenatal fluoride exposure and IQ study of Bashash et al (2017):

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.

These authors reported a statistically significant association of Child IQ with the prenatal fluoride exposure of their mothers. However, their figures showed a very wide scatter in the data indicating very little explanation of the variation in child IQ by the association with prenatal fluoride. (see below left). This must be why the Fluoride Action Network removed the data points from the figure when reproducing it for their promotion of the paper (see below right).

Bashash et al., (29017) did not give the complete statistical analysis of their data. However, I was able to digitally extract the data from their figure and my analysis showed that prenatal fluoride expose was only able to explain a little over 3% of the variation in child IQ. So, despite the statistical significance of their observed relationship prenatal fluoride exposure is unlikely to be a real factor in child IQ. In fact, concentration on this minor (even if statistically significant) factor will only inhibit the discovery of the real causes of IQ variation in these children.

Yes, anti-fluoride campaigners will protest that this study did consider some other possible risk-modifying factors. However the very low-level of explanation of the variation in the data indicates they did not consider enough.

(see Premature births a factor in cognitive deficits observed in areas of endemic fluorosis?,  Fluoride, pregnancy and the IQ of offspring and Maternal urinary fluoride/IQ study – an update).

The Xiang et al., (2003) water fluoride and IQ study:

Xiang, Q; Liang, Y; Chen, L; Wang, C; Chen, B; Chen, X; Zhouc, M. (2003). Effect of fluoride in drinking water on children’s intelligence. Fluoride, 36(2), 84–94.

Anti-fluoride campaigners rely a lot on this and other papers from this group.  Even though this research involved areas of endemic fluorosis it, in a sense, provides some of their best evidence because they reported a dose-dependent relationship of IQ to water F. Xiang et al., (2003) claimed a statistically signficant association of child IQ to fluoride water levels.  Other anti-fluoride campaigners, and some other researchers, have cited Xiang et al., (2003) to support such an association.

I don’t question these researchers found a significant association – but there is a problem. Nowhere do they give a statistical analysis or the data to support their claim! Very frustrating for critical readers (and we should all be critical readers).

They did, however, give some evidence from a statical analysis of the relationship of IQ with urinary fluoride. They did not give a complete statistical analysis but they included the data in a figure  (see below) – so I did my own statistical analysis of data digitally extracted from the figure.

The figure shows a high scatter of data points so this is another case of a statistically significant relationship explaining only a small part of the variability. My analysis indicates the relationship explains only about 3% of the variability in IQ value. Another case where researchers have concentrated on their own pet relationship and in the process not properly searched for more reasonable risk-modifying factors capable of explaining a larger proportion of the variation.

I have made a more detailed critique of Xiang et al.  (2015) and Hirzy et al., (2016) which relies on this data (see Does drinking water fluoride influence IQ? A critique of Hirzy et al. (2016)). A paper based on this has been submitted to a journal for publication and is currently undergoing peer review..

(see Anti-fluoride authors indulge in data manipulation and statistical porkies, Debunking a “classic” fluoride-IQ paper by leading anti-fluoride propagandists,  Connett fiddles the data on fluoride, Connett & Hirzy do a shonky risk assesment for fluoride and Connett misrepresents the fluoride and IQ data yet again).

Conclusion

This  briefly outlines the statistical problems of a number of papers anti-fluoride campaigners rely on. Two common problems are:

• Insufficient consideration of confounders or other risk-modifying factors – indicating a bias towards a “preferred” cause, and
• Reliance on a relationship that, although statistically significant, explains only a very small fraction of the observed variation – again indicating bias towards a “preferred” cause

I don’t for a minute suggest that only those researchers publishing “anti-fluoride” research are guilty of these errors. They are probably quite common. Authors will generally responsibly warn that “correlation does not prove causation” and suggest more work needs to be done including  consideration of a wider number of confounders or risk-modifying factors. However, bias is only human so researcher advocacy for their own findings is understandable. The published research may even be of general value if readers interpret it critically and intelligently.

However, in the political world such critical consideration is very rare. Activists will use published research in the way a drunk uses a lamppost – more for support than for illumination. This makes it important that the rest of us be more objective and critically assess the claims they are making. Part of this critical assessment must include an objective consideration of the published research that is being cited.

Similar articles

Fluoridation not associated with ADHD – a myth put to rest

Fluoridated water is NOT associated with ADHD: Photo by mtl_moe

The myth of community water fluoridation causing attention deficit hyperactivity disorder (ADHD) is just not supported by the data. I show this in a new paper accepted for publication in the British Dental Journal. This should remove any validity for the claims about ADHD by anti-fluoride campaigners.

Mind you, I do not expect them to stop making those claims.

The citation for this new paper is (will be):

Perrott, K. W. (2017). Fluoridation and attention hyperactivity disorder – a critique of Malin and Till. British Dental Journal. In press.

The Background

The fluoridation causes ADHD myth was initially started by the publication of Malin & Till’s paper in 2015:

Malin, A. J., & Till, C. (2015). Exposure to fluoridated water and attention deficit hyperactivity disorder prevalence among children and adolescents in the United States: an ecological association. Environmental Health, 14.

It was quickly taken up and promoted by anti-fluoride campaigners – becoming one of their most cited papers when claiming harmful psychological effects from fluoridation. Part of the reason for its popularity is that it is the only published paper reporting an association between community water fluoridation (CWF) incidence and the prevalence of a psychological deficit. All other reports on this used by anti-fluoride campaigners are based on studies made in high fluoride regions like China where fluorosis is endemic. Those studies are just not relevant to CWF.

While many critics rejected Malin & Till’s conclusions on the simple basis that correlation does not mean causation I decided to look a bit deeper and test their statistical analyses. This was easy because they used published US data for each US state and such data is available for many factors.

I posted my original findings in the article ADHD linked to elevation not fluoridation. This showed that a number of factors were independently associated with ADHD prevalence (eg., home ownership, poverty, educational attainment, personal income, and % of the population older than 65) and these associations were just as significant statistically as the associaiton reported by Malin & Till.

However, multiple regression of possible modifying factors showed no statistically significant of ADHD prevalence with CWF incidence when mean state elevation was includedd.

The importance of elevation was confirmed by Huber et al. (2015):

Huber, R. S., Kim, T.-S., Kim, N., Kuykendall, M. D., Sherwood, S. N., Renshaw, P. F., & Kondo, D. G. (2015). Association Between Altitude and Regional Variation of ADHD in Youth. Journal of Attention Disorders.

Huber et al., (2015) did not include CWF incidence in their analyses. I have done this with the new paper in the British Dental Journal.

Publication problems

I firmly believe that scientific journals, like  Environmental Health which published the Malin & Till paper, have an ethical obligation to accept critiques of papers they publish (subject to peer review of course). Similarly, it is appropriate that any critique of a published paper is made in the journal where it was originally published. Implicit in this arrangement, of course, is that the authors of the original paper get the chance to respond to any critique and that the response be published by the original journal.

Unfortunately, this was not possible for this paper because the Chief Editor of  Environmental Health,  Prof Philippe Grandjean, simply refused to allow this critique to be considered for publication. No question of any peer reviuew. In his rejection he wrote:

“Although our journal does not currently have a time limit for submission of comments on articles published in EH, we are concerned that your response appears a very long time after the publication of the article that you criticize. During that period, new evidence has been published, and you cite some of it. There are additional studies that would also have to be taken into regard in a comprehensive comment, as would usually be the case after two years. In addition, the way the letter is written makes us believe that the letter is part of a controversy, and our journal is certainly not the appropriate forum for a dispute on fluoride policies.”

My response pointed out the reasons for the time gap (problems related to the journals large publication fee), that no other critique of the Malin & Till paper had yet been published and that any perceived polemics in the draft should normally be attended to by reviewers. This was ignored by Grandjean.

While Grandjean’s rejection astounded me – something I thought editors would consider unethical – it was perhaps understandable. Grandjean is directly involved as an author of several papers that activists use to criticise community water fluoridation. Examples are:

• Grandjean, P., & Landrigan, P. J. (2014). Neurobehavioural effects of developmental toxicity. Lancet Neurol, 13(March), 330–338.
• Choi, A. L., Sun, G., Zhang, Y., & Grandjean, P. (2012). Developmental fluoride neurotoxicity: A systematic review and meta-analysis. Environmental Health Perspectives, 120(10), 1362–1368.
• Choi, A. L., Zhang, Y., Sun, G., Bellinger, D., Wang, K., Yang, X. J., … Grandjean, P. (2015). Association of lifetime exposure to fluoride and cognitive functions in Chinese children: A pilot study. Neurotoxicology and Teratology, 47, 96–101.

Grandjean is part of the research group that has published data on IQ deficits in areas of endemic fluorosis – studies central to the anti-fluoride activist claims that CWF damages IQ.  He has also often appears in news reports supporting research findings that are apparently critical of CWF so has an anti-fluoridation public standing.

In my posts Poor peer-review – a case study and Poor peer review – and its consequences I showed how the peer review of the original Malin & Till paper was one-sided and inadequate. I also provided a diagram (see below) showing the relationship of Grandjean as Chief Editor of the Journal, and the reviewers as proponents of chemical toxicity mechanisms of IQ deficits.

So, I guess a lesson learned. But the unethical nature of Grandjean’s response did surprise me.

I then submitted to paper to the British Dental Journal. It was peer-reviewed, revised and here we are.

The guts of the paper

This basically repeated the contents of my article ADHD linked to elevation not fluoridation. However, I tried to use Malin &Till’s paper as an example of problems in ecological or correlation studies. In particular the inadequate consideration of possible risk-modifying factors. Malin & Till clearly had a bias against CWF which they confirmed by limiting the choice of covariates that might show them wrong. I agree that a geographic factor like altitude may not have been obvious to them but their discussion showed a bias towards chemical toxicity mechanisms – even though other social factors are often considered to be implicated in ADHD prevalence.

Unfortunately, Malin & Till’s paper is not an isolated example. Another obvious example of confirmation bias is that of Peckham et al., (2015). They reported an association of hypothyroidism with fluoridation but did not include the most obvious example of iodine deficiency as a risk-modifying factor in their statistical analysis

Of course, anti-fluoride campaigners latched on to the papers of Peckham et al., (2015) and Malin & Till (2015) to “prove” fluoridation was harmful. I guess such biased use of the scientific literature simply to be expected from political activists.

However,  I also believe the scientific literature contains many other examples where inadequate statistical analyses in ecological studies have been used to argue for associations which may not be real. Such papers are easily adopted by activists who are arguing for or against specific social policies or social attitudes. For example, online articles about religion will sometimes refer to published correlations of religosity with IQ, educational level or scoio-economic status. Commenters simply select the studies which confirm the bias they are arguing for.

These sort of ecological or corellations studies can be useful for developing hypotheses for future study but it is wrong to use them to support an argument and worse as “proof” of an argument.

Take home message

1. There is no statistically significant association of CWF with ADHD prevalence. Malin & Till’s study was flawed by lack of consideration of other possible risk-modifying factors;
2. Be very wary of ecological or correlation studies.Correlation is not evidence for causation and many of these sudues iognore other possible important risk-modifying factors.

Similar articles

Leader of flawed fluoridation study gets money for another go

Professor Christine Till has been given a $300,000 grant to test for harmful effects of fluoride.

Malin and Till (2015) published research indicating a relationship between fluoridation and Attention Deficit Hyperactivity Disorder (ADHD). However, that study was flawed because it omitted important confounders. When these are included the relationship disappears.

I analysed that study in my article ADHD linked to elevation not fluoridation where I showed the relationship of ADHD to elevation was much more important than fluoridation. Huber at al., (2015) published work confirming the relationship of ADHD with elevation. So, obviously, elevation is an important confounder and  Malin and Till (2015) did not consider it in their study.

My own analysis indicated that there were a number of other confounders which are related to ADHD – with correlations similar to (eg., educational attainment, proportion of the sate’s population older than 65  and Per Capita personal income) or better (mean state elevation, home ownership and % living in poverty ) than that for fluoridation. That rings alarm bells – why consider only one factor (fluoridation) if there are other factors which appear equally or more important? Isn’t that confirmation bias? (I concede that Malin and Till did include a socioeconomic measure in their statistical analysis – but this was clearly not enough).

I tested the relative importance of the different facts using multiple regression and – sure enough – found that once a few important confounders were included water fluoridation could not explain any of the variance in ADHD! The statistically significant factors were mean elevation, home ownership, and poverty. The contribution of fluoridation was not statistically significant in this multiple regression.

A model including mean state elevation, home ownership and poverty explains about 45% of the variance in ADHD – much better than fluoridation could (Malin and Till explained 27 -32% for the fluoridation data).

Now, I read that Professor Till has been given research finds to have another go and possible harmful effects of fluoride. (see York professor leads study that could help answer fluoride safety questions). She plans to look at data from a Canadian investigation of pregnant women exposed to  contaminants. She says:

“Our study employs a prospective design that includes biomarkers of exposure to fluoride, detailed assessment of potential confounders, a comparison group, and the use of sensitive cognitive and behavioural measures that have been collected in one of the world’s most comprehensively characterized national pregnancy cohorts (MIREC).”

Now, I am pleased she aspires to a “detailed assessment of potential confounders” but wonder how detailed this will be after the problems with the Malin and Till (2015) study.

I have not yet seen any published response to the Malin and Till paper – maybe the cost of publication (US$2020) that journal is discouraging critics. It certainly discouraged me (I do not have institutional support for publication costs). Nevertheless, I hope professor Till has been acquainted with some of the criticism of that paper so that she can pay more attention to important confounders in the coming work

We can draw a few lessons from this.

Be careful of published statistical relationships

These days it is so easy to hunt down data and do this sort of exploratory statistical searching for significant relationships. But a statistically significant relationship is not evidence of a real cause. For example, there is a strong relationship between the sales of organic produce and prevalence of autism – but I have yet to hear anyone seriously suggest the relationship is at all causal.

But the scientific literature is still full of such studies – and I guess the motivated author can easily find arguments and other data in the literature that they, at least, feel convincing enough to justify publication.

Refereeing of scientific papers is, on the whole, abysmal

All authors have a pretty good idea of which journals, and reviewers, will be friendlier to their work – and which would be antagonistic. It is only natural tosubmitt to the friendlier journal.

Unfortunately, the Malin and Till paper was submitted to a journal with editors known to be friendly to a chemical toxicity model of cognitive deficits. Further, it turns out that the reviewers chosen for the paper were also supportive of such an approach.

While one reviewer did suggest including lead as a possible confounder (again showing a chemical toxicity bias) none of them suggested consideration of other confounders more likely to be connected with ADHD.

I discussed the editorial and reviewer problems of the Malin and Till paper in Poor peer-review – a case study. (The journal, Environmental Health, has a transparent peer-review process which provides access to the names and reports of the reviewers.)

Again – another example of readers beware – even readers of scientific papers in credible journals.

Similar articles

ADHD link to fluoridation claim undermined again

Recently I suggested that Attention Deficit Hyperactivity Disorder (ADHD) was better correlated with elevation than with community water fluoridation (see ADHD linked to elevation not fluoridation). I criticised the study of Malin and Till (2015) for limiting their investigation to a chemical toxicity hypothesis and pointed out that once confounding factors like elevation are included their reported relationship between ADHD and community water fluoridation (CWF) disappears.

Seems I am not the only one to notice this. A new paper reports that same relationship:

Huber, R. S., Kim, T.-S., Kim, N., Kuykendall, M. D., Sherwood, S. N., Renshaw, P. F., & Kondo, D. G. (2015). Association Between Altitude and Regional Variation of ADHD in Youth. Journal of Attention Disorders.

They used data sets for the prevalence of ADHD in 2007 and 2010 in US states and found a negative relationship with average state elevation. Their correlation coefficients (R 2 = .38, p < .001; R 2 = .31, p < .001 respectively) are similar to the one I found.

This paper effectively supports my earlier conclusion:

“I do not think Malin and Till (2015) are justified in drawing the conclusion that CWF influences ADHD. Their mistaken conclusion has arisen from their limited choice of data considered for the exploratory analysis. That in itself seems to have resulted from a bias inherent in their hypothesis that “fluoride is a widespread neurotoxin.”

I was not advancing an alternative hypothesis but Huber et al., (2015) did suggest the hypothesis:

“As decreased dopamine (DA) activity has been reported with ADHD and hypoxia has shown to be associated with increased DA, we hypothesized that states at higher altitudes would have lower rates of ADHD.”

But the important lesson is once factors like elevation are taken into account there is no statistically significant relationship with CWF. The Malin & Till (2015) paper currently heavily promoted by anti-fluoride propagandists is flawed.

See also: Rates of ADHD appear to decrease at higher altitudes

 Similar articles

Is comfirmation bias essential to anti-fluoride “research?”

Anti-fluoride propagandists like Declan Waugh and Paul Connett avidly scan the scientific literature looking for anything they can present as evidence for harmful effects of community water fluoridation (CWF). Sometimes they will even do their own “research”  using published and on-line health data looking for any correlations with CWF, or even just with fluoride levels in drinking water.

Several years ago an activist going under the nom de plume “Fugio” posted images showing correlations of mental retardation, adult tooth loss and ADHD with the incidence of CWF in the US. These images are simply the result of “research” driven by confirmation bias and data dredging.They prove nothing. Correlation is not proof of a cause. And no effort was made to see if other factors could give better correlations.

I go through Fugio’s examples below – partly because I noticed one of their images surfacing recently on an anti-fluoridation Facebook page as “proof” that CWF causes tooth loss. But also because they are just more examples of the type of limited exploratory analysis used in two recently published papers – Peckham et al., (2015) (discussed in my article Paper claiming water fluoridation linked to hypothyroidism slammed by experts) and Malin and Till (2015) (discussed in my articles More poor-quality research promoted by anti-fluoride activists, ADHD linked to elevation not fluoridation and Poor peer-review – a case study).

ADHD

This figure is essentially the same as that reported by Malin & Till (2015). In fact, I wonder if Fugio (who posted December 2012) is the unattributed source of Malin & Till’s hypothesis. Fugio chose the ADHD data for 2007 and fluoridation data for 2006 whereas Malin and Till (2015) concentrated mainly on fluoridation data for 1992 which had the highest correlation with ADHD figures.

I won’t discuss this further here – my earlier article ADHD linked to elevation not fluoridation shows there are a number of other factors which correlate with ADHD prevalence just as well or better than CWF incidence does and should have at least been considered as confounding if not the main factors. I found a model using mean elevation, home ownership and poverty only (no CWF included) explained about 48% of the variation whereas their model using CWF and mean income explained only 22-31% of the variation. And when these confounder factors were considered the correlation of ADHD with CWF was not statistically significant.

In other words we could do a far better job of predicting ADHD prevalence without involving CWF.

Water Fluoridation and Adult Tooth Loss

Fugio posted a figure showing a correlation of adult tooth loss with CWF incidence in 2008. It was statistically significant explaining 11% of the variation. But quite a few other factors display better correlations with adult tooth loss. For example, the data for smoking by itself explains 66% of the variation (see figures below).

Checking out correlations with a range of factors I found a model involving only smoking and longitude  explaining  about 74% of the variation. The contribution from CWF was not significant statistically – it added nothing to this model.

Water Fluoridation and Mental Retardation

Fugio found a better relationship between CWF in 1992 and mental retardation in 1993 – a correlation explaining 19% of the variation. Apparently the concept of “mental retardation” was later abandoned as there do not appear to be any more recent statistics.

But again, if Fugio had not stopped there he/she would have found a number of other factors with better correlations. I give an example in the figure where state educational level (% Bachelors Degree in 1993) explained 50% if the variation. This correlation is negative as we might expect.

 Again I used multiple regression analysis to derive a model involving educational level (% with Bachelors degree in 1993), poverty in 1993 and mean state elevation which explained 69% of the variation. No statistically significant contribution from CWF occurred.

Conclusions

I am not suggesting here that the factors I identified have a causal effect. Simply that they give better correlations  than CWF. These and similar confounding factors should have been considered by Fugio and Malin and Till (2015).

My purpose is to show that this sort of exploratory analysis of easily available data can easily produce results for anti-fluoride activists who are searching for some “sciency” looking arguments to back up  their position. Provided they don’t look too deeply, stop while they are ahead and refuse to consider the influence of other factors.

Unfortunately poor peer review by some journals is allowing publication of work that is no better than this. Peckham et al (2015) did nothing to check out other factors except gender in their correlations of hypothyroidism with CWF. The glaring omission was of course dietary iodine which is known to have a causative link with hypothyroidism. (I could not find US data for hypothyroidism so was unable to check out Peckham et al’s hypothesis for the US.) Malin and Till (2015) included only socioeconomic status (as indicated by income) in their analysis despite the fact that ADHD is known to be related to a number of factors like smoking and alcohol intake.

As I keep saying, when it comes to understanding the scientific literature it really is a matter of “reader beware.” It’s easy to find papers supporting one’s pet obsession if you are not critical and sensible with your literature searches. And it is important not to take at face value the claims of activists who clearly rely on confirmation bias when they explore the literature.