Philosophical Transactions: Lithium in Scottish Drinking Water with Al Hatfield

Previous Philosophical Transactions:

Al Hatfield is a wannabe rationalist (his words) from the UK who sent us some data about water sources in Scotland. We had an interesting exchange with him about these data and, with Al’s permission, wanted to share it with all of you! Here it is:


Hi,

I know you’re not that keen on correlations and I actually stopped working on this a few months ago when you mentioned that in the last A Chemical Hunger post, but after reading your post today I wanted to share it anyway, just in case it does help you at all. 

It’s a while since I read all of A Chemical Hunger but I think this data about Scottish water may support a few things you said:

– The amount of Lithium in Scottish water is in the top 4 correlations I found with obesity (out of about 40 substances measured in the water)

– I recall you predicted the top correlation would be about 0.5, the data I have implies it’s 0.55, so about right.

– I recall you said more than one substance in the water may contribute to obesity, my data suggested 4 substances/factors had correlations of more than 0.46 with obesity levels and 6 were more than 0.41.

Method

– Scottish Water test and record how much of up to 43 substances is in each reservoir/water source in Scotland https://www.scottishwater.co.uk/your-home/your-water/water-quality/water-quality

– their data is in pdf format but I converted it to Excel

– Scottish Water don’t publish Lithium levels online but I did a Freedom of Information request and they emailed it to me and I added it to the spreadsheet.

– I used the website to get the water quality data for a reservoir for every city/big town in Scotland and lined it up in the spreadsheet.

– I used Scottish Health Survey – Local Area Level data to find out what percentage of people are obese in each area of Scotland and then matched it as well as I could to a reservoir/water source.

– I then used the Data Analytics add-on in Excel to work out the correlations between the substances in the water and obesity.

Correlations with obesity (also in attachment)

Conductivity 0.55

Chloride 0.52

Boron 0.47

Lithium 0.47

Total Trihalomethanes 0.42

Sodium 0.42

Sulphate 0.38

Fluoride 0.37

Colony Counts After 3 Days At 22øc 0.34

Antimony 0.33

Gross Beta Activity 0.33

Total organic carbon 0.31

Gross Alpha Activity 0.30

Cyanide 0.26

Iron 0.26

Residual Disinfectant – Free 0.23

Arsenic 0.23

Pesticides – Total Substances 0.23

Coliform Bacteria (Total coliforms) 0.23

Copper 0.19

PAH – Sum Of 4 Substances 0.19

Nitrite 0.17

Colony Counts After 48 Hours At 37øc 0.16

Nickel 0.13

Nitrite/Nitrat e formula 0.13

Nitrate 0.12

Cadmium 0.11

Turbidity 0.08

Bromate 0.08

Colour 0.06

Lead -0.10

Manganese -0.12

Hydrogen ion (pH) -0.12

Aluminium -0.15

Chromium -0.15

Ammonium (total) -0.22

2_4-Db -0.25

Residual Disinfectant – Total -0.36

2_4-D -0.42

Dicamba -0.42

MCPB -0.42

MCPP(Mecoprop) -0.42

Scottish Water definition of Conductivity

Conductivity is proportional to the dissolved solids content of the water and is often used as an indication of the presence of dissolved minerals, such as calcium, magnesium and sodium.

Anyway, not sure if that’s any help to you at all but I enjoy your blog and thought I would send it in. Let me know if you have any questions.

Thanks 

Al


Hi Al,

Wow, thanks for this! We’ll take a look and do a little more analysis if that’s all right, and get back to you shortly. 

Do you know the units for the different measurements here, especially for the lithium? We’d be interested in seeing the original PDFs as well if that’s not too much hassle.

Thanks! 

SMTM


Hi,

You’re welcome! That’s great if you can analyse it as I am very much an amateur. 

The units for the Lithium measurements are µgLi/l. I’ve attached the Lithium levels Scottish Water sent me. I think they cover every water source they test in Scotland (though my analysis only covered about 15 water sources).

Sorry I don’t have access to the original pdfs as they’re on my other computer and I’m away at the moment. But I have downloaded a couple of pdfs online. Unfortunately the online versions have been updated since I did my analysis in late November, but hopefully you can get the idea from them and see what measurements Scottish Water use.

Let me know if you’d like anything else.

Thanks,

Al


Hey Al,

So we’ve taken a closer look at the data and while everything is encouraging, we don’t feel that we’re able to draw any strong conclusions.

We also get a correlation of 0.47 between obesity and lithium levels in the water. The problem is, this relationship isn’t significant, p = 0.078. Basically this means that the data are consistent with a correlation anywhere between -0.06 and 0.79, and since that includes zero (no relationship), we say that it’s not significant.

This still looks relatively good for the hypothesis — most of the confidence interval is positive, and these data are in theory consistent with a correlation as high as 0.79. But on the whole it’s weak evidence, and doesn’t meet the accepted standards.

The main reason this isn’t significant is that there are only 15 towns in the dataset. As far as sample sizes go, this is very small. That’s just not much information to work with, which is why the correlation isn’t significant. For similar reasons, we haven’t done any more complicated analyses, because we won’t be able to find much with such a small sample to work with. 

Another problem is that correlation is designed to work with bivariate normal distributions — two variables, both of them approximately normally distributed, like so: 

Usually this doesn’t matter a ton. Even if you’re looking at a correlation where the two variables aren’t really normally distributed, it’s usually ok. And sometimes you can use transformations to make the data more normal before doing your analysis. But in this case, the distribution doesn’t look like a bivariate normal at all:  

Only four towns in the dataset have seriously elevated lithium levels, and those are the four fattest towns in the dataset. So this is definitely consistent with the hypothesis.

But the distribution is very strange and very extreme. In our opinion, you can’t really interpret a correlation you get from data that looks like this, because while you can calculate a correlation coefficient, correlation was never intended to describe data that are distributed like this.

On the other hand, we asked a friend about this and he said that he thinks a correlation is fine as long as the residuals are normal (we won’t get into that here), and they pretty much are normal, so maybe a correlation is fine in this case? 

A possible way around this problem is nonparametric correlation tests, which don’t assume a bivariate normal distribution in the first place. Theoretically these should be kosher to use in this scenario because none of their assumptions are violated, though we admit we don’t use nonparametric methods very often. 

Anyways, both of the nonparametric correlation tests we tried were statistically significant — Kendall rank correlation was significant (tau = 0.53, p = .015), and so was the Spearman rank correlation (rho = 0.64, p = .011). Per these tests, obesity and lithium levels are positively correlated in this dataset. The friend we talked to said that in his opinion, nonparametric tests are the more conservative option, so the fact that these are significant does seem suggestive. 

We’re still hesitant to draw any strong conclusions here. Even if the correlations are significant, we’re working with only 15 observations. The lithium levels only go up to 7 ppb in these data, which is still pretty low, at least compared to lithium levels in many other areas. So overall, our conclusion is that this is certainly in line with the lithium hypothesis, but not terribly strong evidence either way.

A larger dataset of more than 15 towns would give us a bit more flexibility in terms of analysis. But we’re not sure it would be worth your time to put it together. It would be interesting if the correlation were still significant with 30 or 40 towns, and we could account for some of the other variables like Boron and Chloride. But, as we’ve mentioned before, in this case there are several reasons that a correlation might appear to be much smaller than it actually is. And in general, we think it can sometimes be misleading to use correlation outside the limited set of problems it was designed for (for example, in homeostatic systems).

That said, if you do decide to expand the dataset to more towns, we’d be happy to do more analysis. And above all else, thank you for sharing this with us!

SMTM

[Addendum: In case anyone is interested in the distribution in the full lithium dataset, here’s a quick plot of lithium levels by Scottish Unitary Authority: 

]


Thanks so much for looking at it. Sounds like I need to brush up on my statistics! Depending how bored I get I may extend it to 40 towns some time, but for now I’ll stick with experimenting with a water filter.

All the best,

Al

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