Download Dirty Electricity: Electrification and the Diseases of by Samuel Milham PDF

By Samuel Milham

Whilst Thomas Edison started wiring manhattan urban with an immediate present electrical energy distribution approach within the Eighteen Eighties, he gave humankind the magic of electrical gentle, warmth, and gear; within the procedure, although, he inadvertently opened a Pandora's field of unbelievable disease and demise. soiled electrical energy tells the tale of Dr. Samuel Milham, the scientist who first alerted the realm in regards to the scary hyperlink among occupational publicity to electromagnetic fields and human affliction. Milham takes readers via his early years and schooling, following the twisting course that ended in his discovery that almost all of the 20th century illnesses of civilization, together with melanoma, heart problems, diabetes, and suicide, are as a result of electromagnetic box publicity. within the moment variation, he explains how electric publicity does its harm, and the way electrical energy is inflicting our present epidemics of bronchial asthma, diabetes and weight problems. Dr. Milham warns that due to the fresh proliferation of radio frequency radiation from cellphones and towers, terrestrial antennas, wireless and Wi-max platforms, broadband web over strength strains, and private digital gear, we will be dealing with a looming epidemic of morbidity and mortality. In soiled electrical energy, he finds the stairs we needs to take, individually and as a society, to coexist with this outstanding yet risky know-how.

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Additional resources for Dirty Electricity: Electrification and the Diseases of Civilization

Sample text

To repeat a classic example, numerous epidemiological studies find an association between coffee drinking and cancer, but this is due to the confounding effect of cigarette smoking: more coffee drinkers smoke cigarettes, and it is the cigarettes, completely and entirely, that cause the cancer; coffee itself has not increased cancer risk. This is confounding bias. Let us suppose that the risk of cancer is higher in women smokers than in men smokers; this is no longer confounding bias, but EM. There is some interaction between gender and cigarette smoking, such that women are more prone biologically to the harmful effects of cigarettes (this is a hypothetical example).

But we have no reason to believe that being female per se leads to cancer, as opposed to being male. Gender itself does not cause cancer; it is not a confounding factor; it merely modifies the risk of cancer with the exposure, cigarette smoking. 1. 1 Effect modification. Effect modifier When a variable affects the relationship between exposure and outcome, then a conceptual assessment needs to be made about whether the third variable directly causes the outcome but is not caused by the exposure (then it is a confounding factor), or whether the third variable does not cause the exposure and seems to modify the exposure’s effects (then it is an effect modifier).

Suppose 25% of one group in our sample had a history of hospitalization for the illness being studied (and thus could be seen as more severely ill than those without past hospitalization); if the other group had a 31% rate of past hospitalization, the difference between the two groups is 6%, and we would be concerned about a difference between the groups of even 3% (10% of the absolute rate, which is 25% in one group and 31% in another group, or around 30% overall), and thus we definitely would be concerned about the observed 6% difference between the groups in past hospitalization (31% – 25% = 6%).

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