But the sort of survey you are proposing does not have the statistical power and methodological rigour to reliably identify any of them.
Sure, this is just a forum poll. It cannot in any way be compared to a properly conducted scientific study. At best it might give you an inkling of some trends; at worst, as you say, it might mislead you. But that's true of all polls on this forum.
One skyscraper is enough to make this not true. Population density is not uniform. No matter how small you go. Not even apartments in the same building have same number of occupants.
We are conceptually dealing with average population densities over many base stations, so one skyscraper near one particular base station makes no difference at all to that average. That's the first thing that needs to be appreciated, in order to understand the method I am using.
I don't know if a slightly more mathematical description might help explain my method, but here goes:
The number of people P living in any annulus located at distance x from a base station is equal to:
P = D π (x + w)^2 – D π x^2
Where:
x = radius of the inner circle of the annulus (and x = distance from the base station at the center of the circle)
w = width of the annulus (= radius of outer circle of the annulus – radius of inner circle of the annulus)
D = local population density in the immediate area of base station (eg, number of people per meter squared)
Blue annulus (ring) at distance x
from a base station at point O
That equation for P just represents the area of a large circle of radius x + w, minus the area of a smaller circle of radius x, which is how you calculate the area of an annulus. Then we multiply the area of the annulus by D, the local population density, in order to work out how many people live inside the annulus.
Now I agree that normally, you would need to know the local population density D before you can proceed. However, because what I am doing is in effect taking the ratio of two annuli adjacent to each other, when you calculate this ratio, in your equation, the D cancels out, so you don't need to know D. You can get your answer without knowing the local population density D (provided the local population density is on average uniform over a 500m radius from the base station).
So what I am doing is taking the ME/CFS incidence in the inner white 300m radius circle (white circle in above diagram) annulus, and dividing by the incidence in the 300m to 500m annulus (blue annulus in the diagram).
That's the beauty of this method: you can get an answer without knowing the local population density, if you assume that
on average, when averaged over many base stations, the local population density is uniform within 500 meters of the base station.
being 500m away or further from the mast is definitely not the same as never being near one at all.
OK, I'll take your word for that. I tend to think quite mathematically, and so for me, the first option definitely incorporates the second. But you are saying that we also have to cater for people who do not think so logically. Fair enough.
Another way to ellimnate the effect of other variables (e.g living near a main road with exposure to exhaust fumes, the effect of population densities on stress levels, poverty level affecting nutrition etc etc) of this sort is to brainstorm them and then capture the information in the questionnaire screening questions and use the information in a complex data analysis at the end, effectively cutting the variables out.
I agree that such confounding factors as vehicle exhaust fumes could potentially effect the results. Since there is some tendency to locate base stations in town centers, or on busy high streets, it's conceivable that closer proximity to a base station might also mean that you tend to live in an area of higher vehicle exhaust pollution. So this could potentially be a confounding factor.
Apologies for the clumsy read above....I still haven't worked out multiquote
Just select the text you want to quote, and click on the pop-up "reply" button that appears next to it, and that will insert the text as a quote within the edit area.
As you can see finding a control is problematic. In this case you need to have a group that didn't get CFS/ me and ask them how far they live away from masts. Just because it's difficult doesn't mean you don't need a control, rather you might reject the methodology as being non substantiative and cancel the research on that basis. Controls eliminate some of the many variables.
I think what you are suggesting is that we need to find the average population distribution around base stations, by examining millions of base stations, and calculating the average distribution. I agree, that would be the more rigorous approach.
My key assumption in this poll is that
on average, this distribution is even and uniform within a 500m radius circle of a base station. That is to say, on average, the population is not for example more concentrated towards the peripheries of the 500m radius circle, nor more concentrated towards the center of this circle. I think this assumption is likely more or less correct, but there could be some unevenness in distribution, which is why I agree that a more accurate approach would be to first determine the average population distribution around millions of base stations.
in addition masts are not uniformly or randomly sited, the goal of a mast is to serve the maximum number of consumers so they are deliberately sited where population density is highest, that alone gives everyone a higher probability of living near a mast.
If you are talking about distances of a few kilometers radius from the base station, then I agree that in especially countryside locations and around small villages, there will tend to be more population density in the center of that several kilometer radius circle. But when we are dealing with a 500m radius, I don't think this concern applies.
If you actually have look at some random base stations on the
UK map, both in suburban areas and out in the countryside near small towns or residential areas, it becomes apparent that there is no obvious scheme for where these base stations are located. Some are located away from residential areas, others are located within residential areas.
But yes, in order to conduct a proper study, you would first need to work out the average population distribution in the 500m radius around base stations, by examining millions of base stations to work out your average. This actually could be done automatically with a bit of software, that takes its data from a base station map.
Lets be serious, to get meaningful results we are going to need a random selection of PWME who meet a reliable criteria, we'll need a matched control group who are similarly geographically distributed, we need all cell mast measurements made by a blinded third party and we'll need several hundred participants to get to statistical significance.
Of course. That's what a proper study would entail. Although if I were doing a proper study, I would not use this method at all; this method is just a rough and ready approach for use in this rough and ready poll.
If I were conducting a study, I'd use newly diagnosed ME/CFS patients, and I'd arrange to go to their home and precisely measure, using electronic measuring equipment, the ambient levels of mobile phone base station radiation within their home, and within their bedroom, and even at their workplace, as well as ambient levels of other electromagnetic fields, such as WiFi radiation, cordless phone radiation, and so forth. That would be a much more rigorous approach.
Surprisingly enough, although mobile phones have been with us since the late 1980s, no research has yet examined the possible links between mobile phone base station radiation, and the incidence of chronic diseases (except for the disease of cancer). This is
according to the WHO.
So if mobile phone base station proximity were a risk factor in the triggering of ME/CFS, or in the triggering of autoimmune diseases, we would not know about it, because there have been no studies. We don't know whether or not mobile phone base station radiation, or other wireless device electromagnetic radiation, is a risk factor for triggering ME/CFS or triggering other chronic diseases or autoimmunity.
