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by Greg Lapin, N9GL, Chairman, ARRL RF Safety Committee
Last month I talked about scientific investigation. In particular, I noted that there is not a good method to study diseases that are caused by life-long exposures to environmental factors. Would it surprise you to learn that science was convinced of the linkage between cigarette smoking and disease 20 years before it was proven?
Epidemiology is the branch of science that has the advantage of examining the results of a lifetime of exposures and the disadvantage of not being able to fully account for all the things to which each subject was exposed. It is epidemiology that often recognizes the long-term danger of exposure to a given substance, and it is generally agreed that it is incapable of proving that a given exposure causes a disease--even though it is a powerful tool for recognizing these interactions. At best, epidemiology serves the purpose of suggesting the relationship, which must then be confirmed under laboratory conditions.
One of the very surprising things that I have learned about scientific investigation into public health is that the links between cigarette smoking and the diseases that it causes had not been proven until recently. This does not mean that I ever doubted that cigarette smoking was terrible public health risk. What it means is that the majority of the evidence about the adverse effects of cigarette smoking was based on epidemiological studies. Because of the long-term nature of the diseases associated with smoking, it was not feasible to determine cause-and-effect in the laboratory for over 20 years.
One might ask, "How can people use epidemiology to condemn smoking and then say that epidemiology is not proof enough to say that RF is bad?" That's a fair question, and leads to the concept of the risk ratio. Instead of simply saying that a higher percentage of a group was found to have a disease than the expected percentage in the general population, the numbers can be compared quantitatively. When calculated properly, a risk ratio indicates the amount of increased chance of finding the disease within the group being examined. A risk ratio of 1 indicates that group members get the disease at the same rate as members of the general population. A risk ratio of 10 means group members have 10 times the incidence of the disease than do members of the general population. Even though epidemiological studies on smokers do not prove cause-and-effect, the risk ratios in almost all of the studies were greater than 10. In comparison, most epidemiological studies on electromagnetic effects have risk ratios of 1, and even the studies that have indicated an association had risk ratios less than 2 in almost all cases.
The field of epidemiology relies on standard population statistics as a basis of comparison. The Centers for Disease Control and Prevention publish these statistics, based on the death records of everyone in the United States (see http://www.cdc.gov/nchs/datawh/statab/unpubd/mortabs.htm). For instance, in the United States there is an annual (1997) rate of death from heart attacks of 272 per 100,000. If you find that a group of people has a higher incidence of heart attacks, you might get suspicious that a common aspect of this group is responsible for causing many of their heart attacks. Looking at the raw numbers like this can be misleading. One of the first things that must be done to a number like this is to correct it for age. As people live longer, they are more likely to die of something. Adjusting the raw numbers to take this into account, the 1997 rate of death from heart attacks in the United States drops to 131 per 100,000.
Another thing that affects statistical studies is clustering. Particularly as the group size drops, the danger increases of misinterpreting statistics based on random differences in the numbers. To illustrate this, let me tell you a true story from my past:
After having been married for a few years, my wife and I decided to expand our family. When Jill was pregnant with our first daughter, we decided to move from our apartment in the city to a house in the suburbs. We spent innumerable weekends with our real estate agent looking at houses and finally found one that we liked in a nice little community north of Chicago. We embarked on the buying process and eventually had a lot of our time and all of our money invested in that house.
Jill was eight months pregnant when we moved in. As a new father-to-be I was hit with a fit of protectiveness for my small but growing family. Right from the start, life in the suburbs was wonderful. The month of May in Chicago is usually sunny with signs of spring bursting forth all over that are very welcome after a long, cold winter. I spent much of my time at the new house outside in the front lawn cutting the grass, planting flowers, and generally enjoying the setting.
One fine afternoon a neighbor stopped by to introduce herself. Here was another effect of the suburbs that I had expected to delight in--friendly neighbors. The older woman from across the street introduced herself and asked the usual questions. After taking in my general air of joy at being there, she decided to truly welcome me to the neighborhood. "You know that you've moved onto THE CANCER STREET?" she allowed.
I thought her hair looked a little strange; it was a wig that could have been covering up some of the aftereffects of chemotherapy. She then proceeded to point out all the houses on the other side of the street--hers included--where at least one of the occupants had come down with the dreaded disease.
At this time I was well on my way to a doctoral degree in biomedical engineering and knew something about statistics. My laboratory also was in a children's hospital, and I daily witnessed the pathetic sight of small children fighting cancer. Needless to say, I was concerned, maybe irrationally, but nevertheless a bad feeling came over me.
I left that conversation in a decidedly more somber mood than when I had entered it. I told my wife about the neighbor's visiting, but I didn't mention the content of the conversation. In her pregnant state that was the last thing she needed to hear.
I continued my daily routine of tending the lawn and gardens after returning home from work. A short time later, I met some other new neighbors. A younger couple was out one day taking a walk. They noticed a new face and came over to say hello. They lived at the end of the street and had been there for a number of years. We talked a bit about the neighborhood and then I decided to broach the subject of my previous conversation with a neighbor. I asked them what they knew about all the people who had contracted cancer on our street. They asked me what I meant, and I related what I had heard the other day.
Upon hearing this tale, the husband looked at his wife and, after a short pause, they both nodded. Their faces were somber and I feared the worst. The husband turned back to me and drew a deep breath. I girded myself for what was sure to be more bad news. "Yeah, I think she was probably right. You made a big mistake by moving to this street," he said.
My fears were confirmed, and my heart slowly rose into my throat.
"What you really should have done was to find a house on the next street over," he continued.
"Why?" I managed.
"Well, that's THE WIFE SWAPPING STREET!" he responded.
The street that I had moved to represented a statistical cluster (as did the next street!). The odds against the random things that cause cancer afflicting people in four houses in a row are high, but not infinite. If a cancer-causing influence were on that side of the street, such as the houses being built on a toxic waste dump, it would be difficult to distinguish that from a statistical anomaly. Another point that needs to be clarified in this situation is that you cannot treat the many different types of cancer as one disease. Different types of cancer tend to be caused by different factors, even if the basic cause is DNA mutation. If the four houses on the other side of my street were the sites of four different types of cancer, it would be even less likely that a single factor was the cause of the disease.
Another type of cluster took place last year in my ham radio club. In the last five months of 1999, five members of the North Shore Radio Club died. In a club of about 180 members, this represents almost 3%, which is quite high. Does this mean anything? There are people who would look at these numbers and shout "Aha!--I told you this electromagnetic energy was killing us all!"
I'd like to remember my friends and fellow hams, each one whom touched my life and enriched it. Ralph Bruebach, KC9DI, died at the age of 52 of a heart attack. Bob Wegner, W9MON, died at the age of 55 of lymphatic cancer. Bob Jacobson, AA9SN died at the age of 73 of a heart attack. Hiram Kennicott, W9RBD, died at the age of 82 of cancer. Mike Hexter, W9FKC, died at the age of 91 of a heart attack.
These deaths represent another form of cluster, a cluster in time. Even though NSRC had club members dying at a rate of one per month during this period--which converts to a death rate of 2,778 per 100,000 for 1999--these were the only club members who died in the past five years. Over the longer period this rate averages to 556, less than the rate in the general population (US) of 865 deaths per 100,000. These numbers are not age-adjusted, which would make a difference by considering that three of the club members had reached the life expectancy of a white male in the United States, and two of them had surpassed this age by 9 and 18 years, respectively.
The statistics required to correctly compare NSRC to the general population are quite involved. The ham club contains no members under the age of 15, has very few female members, does not have the same racial balance as in the general population, has a high level of education among its members, and is not economically diverse. Since all of these factors affect death rates, it is necessary to make corrections to avoid comparing apples with oranges.
The old saying goes, "Figures don't lie, but liars can figure." Even if it is not someone's intention to lie, it is easy to see how many different ways these types of numbers can be presented and how they can be innocently misinterpreted. In addition to the corrections that must be made to epidemiological data to account for all the factors in one's life that may affect disease, the final numbers must be correctly processed. Numerical techniques must be used to deal with statistical anomalies, like clusters, and to compare rates of death and disease to the correct population statistics.
When reporters, who are unlikely to understand all of the confounding factors that are included in a scientific paper, publish epidemiological results in the popular press, it is not surprising that many members of the public have developed a fear of electromagnetic radiation.
So, here's to the memory of my friends. May they be as happy wherever they are now as they were on Earth, doing ham radio.
Editor's note: Greg Lapin, N9GL, started working in the RF Safety world after spending many years first studying cardiac function imaging and then brain tumor kinetics. He is currently chairman of the ARRL RF Safety Committee and a member of the IEEE Committee on Man and Radiation. A former professor of Biomedical Engineering and Neurology at Northwestern University, he now works as a consulting professional engineer in the electronics industry. He was first licensed as a ham when he was a teenager in 1969 and continues to be fascinated by all aspects of Amateur Radio. One of his many interests is electronic design, and he is the author of Chapter 8, "Analog Signal Theory and Components" in The ARRL Handbook for Radio Amateurs. His non-ham interests include making things grow in his garden and serving as Commissioner of the local children's softball league. At other times when he is not working or doing his kids' homework, you might find him with the local emergency services agency, climbing his tower, building a new QRP rig, playing with his APRS setup, responding to QSL cards, going off on a DXPedition, or trying to get that new one. You can reach Greg Lapin, N9GL, via email at g.lapin@ieee.org
