Some Basic Concepts
Perhaps the worst epidemic to ever inflict humanity was the outbreak of bubonic plague, or the black death, that swept through Europe in the middle of the fourteenth century. Although precise figures on the number of deaths are almost impossible to determine, it has been estimated that one quarter to one third of the population of Europe, or 15 million to 20 million people, died. Yet as terrible as these figures are, two points are readily apparent. First, not everyone was affected. Even in Florence, Italy, the death rate was nearly 70 percent, but of course 30 percent of the populace was not affected. Second, the plague was not always present. It came in waves during a period of a few years, disappearing in the winter and reappearing in the spring, until it finally faded from the scene. The question this raises is why—why some people and not others, why in 1347 and not the previous year, why in the warm months but not winter, why humans and not dogs, and on and on.
Let’s take a look at some of the factors that might provide an explanation for some of these questions by using the plague and other disorders as models. Authors tend to group the factors into triads—person, place, and time or agent, host, and environment. “Place” though is just another name for “environment,” and “host” is a fancy term for “person” (because we’re not discussing animal epidemiology in this book). So after eliminating duplications, we are left with a tradition-breaking tetrad: agent, person, place, and time.
AgentIt seems as if every time we open the newspaper, we’re greeted with news that something else is going to kill us: if we stay at home, it will be radon gas from the basement or Cryptosporidium from the drinking water; if we go out, it will be the cholesterol in the popcorn at the movies or hemolytic uremic syndrome from E. coli when we eat at the local hamburger joint. Yet a moment’s reflection will tell us that, despite the tenor of these stories, not everything is deadly or even dangerous (otherwise, we wouldn’t be here to write this book or you to read it). There are only certain things that are necessary etiologic factors for disease—what epidemiologists call agents. Lilienfeld groups agents into four categories: (1) nutritive, (2) chemical, (3) physical, (4) infectious.
Some nutritive agents can cause disease by an excess of them and others by a deficiency. Too much cholesterol, for example, may lead to coronary heart disease; too much salt may lead to hypertension; too much calcium may lead to kidney stones. On the other hand, too little calcium can lead to osteoporosis (how’s that for a bind); beriberi can result from too little thiamine (vitamin B1); pellagra can result from too little niacin; kwashiorkor can result from a protein deficiency. Chemical agents may consist of allergens (e.g., ragweed, various food dyes, bee venom) or poisons (e.g., arsenic, carbon monoxide, overdoses of heroin or tricyclic antidepressants). Ionizing radiation or ultraviolet light would be considered among the physical agents that can lead to problems. Perhaps the most well-known agents to health workers are the infectious agents, such as viruses (mumps, measles, Ebola virus, and acquired immunodeficiency syndrome [AIDS]), bacteria (tuberculosis [TB], rheumatic fever, syphilis), protozoa (malaria), or rickettsia (typhus, Rocky Mountain spotted fever).
PersonAgents are necessary to cause disease, but they are not sufficient. Not everyone who is stung by a bee develops an anaphylactic reaction, and two people may enjoy the same meal in Mexico City, but only one may spend the rest of the evening enthroned upon that porcelain perch. It is obvious that people differ in terms of their susceptibility or response to the agents—what are called the person or host factors.
There are some person characteristics that we are born with, like gender (what we used to call “sex”), year of birth, religion, and genetic make-up. For example, there are conditions that occur with a greater frequency within one ethnic group rather than another (e.g., sickle cell anemia among African-Americans, Tay-Sachs disease among Jews from central Europe, thalassemia among Greeks and Italians) or more in one sex than the other (e.g., hemophilia), suggesting a genetic component. Year of birth is somewhat different from age, although the two are obviously related. If I am 50 years old this year (OK, so I shaved off a few years), and you are 30 years old, then in 20 years you will be the same age I am now and susceptible to the same aches and pains. However, what can never change is that you belong to a different birth cohort. Fig.1174 shows the death rate per 100,000 men at different ages (the X-axis) among three cohorts: men born in the decades 1885 to 1894,1895 to 1904, and 1905 to 1914. The graph indicates that for a man between the ages of 15 and 24 who was born between 1885 and 1894, the death rate was 539.2 per 100,000 men, whereas for someone the same age but born between 1905 and 1914, the death rate was 349.3 per 100,000. Thus although their ages are the same, what they experience is determined in part by when they were born.
Figure 1174 – (Figure 2-3) Mortality rates of men at different ages born in various decades
Data from Spiegelman M, Erhardt CL: Mortality and longevity in the United States. In Erhardt CL, Berlin JE, editors: Mortality and morbidity in the United States, Cambridge, Mass, 1974, Harvard University Press.
Some figures may not display clearly when rendered as a PDF or printed.
Other host factors are modified or acquired with time, such as age itself and immunologic experience, whether achieved naturally through prior exposure (as occurred with some people in Panum’s study of measles) or artificially through inoculation. Chronic illnesses, such as emphysema, which increase a person’s susceptibility to other disorders, would be another example of an acquired host factor. Fig.1178 shows a condition that, although not a disease in the classical sense, is a state that is affected by age. The data, taken from Giambra, show the proportion of people responding “Usually True”, “True”, or “Very True” to the statement, “Whenever I am bored, I daydream about the opposite sex.” The implications of this for one’s mental health are best left to those people more than 45 years of age, who obviously have more time to daydream about these more mundane matters.
Looking at the number of people who acquire a disease at different ages can also suggest hypotheses about etiology. For example, Fig.1205shows the incidence of Hodgkin’s disease in Brooklyn between 1943 and 1952. Two peaks stand out, one in the late 20s and another much later in life. This has led to speculation that there are two different processes occurring, a biologic agent of low infectivity early on and a mechanism more like that of other lymphomas that affects older people.
Figure 1178 – (Figure 2-4) Percent of people of different ages who daydream about the opposite sex when bored
Data from Giambra LM: Daydreaming across the life span: late adolescent to senior citizen, Int J Aging Hum Dev 5:115-140, 1974.
Some figures may not display clearly when rendered as a PDF or printed.
Figure 1205 – (Figure 2-5) Incidence of Hodgkin’s disease by age
Data from MacMahon B: Epidemiological evidence on the nature of Hodgkin’s disease, Cancer 10:1045-1054, 1957.
Some figures may not display clearly when rendered as a PDF or printed.
A third class of person factors is more transitory, like time-limited comorbid conditions, fatigue, or nutritional status. It was likely that factors such as these accounted for varying death rates from the plague from one town to another and why some people survived while others in the same household did not. Finally, some host factors depend on our behavior— whether we exercise, how we make use of health services, what we eat, and so on.
These categories obviously overlap. Certain behaviors, for instance, have much to do with religion, which for most people is acquired at birth and does not change. Some religions are strictly vegetarian and others prohibit smoking and drinking. So look at these more as conceptual guides, rather than fixed differentiations among person variables.
PlaceIf we look at Fig.1207, we can see that there is a strong association between the estimated daily fat intake for women in 39 different countries and breast cancer mortality rates, which highlights the role of place. It is obvious that place, which is also referred to as environment, is rarely a direct factor in its own right. Rather, it reflects a host of other factors that are distributed geographically, including (1) climate (as Hippocrates himself pointed out), (2) diet (as in the case of breast cancer and neural tube defects), (3) cultural practices, (4) methods of food preparation and storage, (5) population density, (6) exposure to pollutants, (7) the presence of arthropods (a fancy name for insects and bugs) that carry disease, and on and on.
Figure 1207 – (Figure 2-6) Geographic comparisons demonstrate a strong relationship between breast cancer mortality rates and the intake of dietary fat
Data from Cohen LA: Diet and cancer, Sci Am 27:42-48, 1987.
Some figures may not display clearly when rendered as a PDF or printed.
The challenge for epidemiologists is, once having found differences in the prevalence of some disorder from one place to another, to discover what it is about the environment that gives rise to these variations. Sometimes this is relatively straightforward. For example, trypanosomiasis occurs only in regions where the tsetse fly lives (if you haven’t guessed, that’s a fancy name for sleeping sickness). Other relationships require much more ingenuity to uncover. It was known for some time that people living in certain regions of the Far East were at a much higher risk for developing cancers in the gastrointestinal tract than people living elsewhere, even higher than people of Asian descent residing in other countries. This latter would eliminate genetics as a factor, but it would still leave pathogens in the soil, pollutants from nearby factories, diet, or a plethora of other potential agents as contributors. As it turned out, the culprit was diet—specifically, the pickled foods that are considered a delicacy and eaten in relatively large quantities.
TimeVariations in the time and occurrence of a particular disease or condition can suggest casual relationships among variables (obviously, this overlaps with year of birth in some cases). As we’ll see in Chapter 5, cause and effect cannot be proven simply by finding correlations over time because many other factors are also changing during the interval of which we may or may not be aware; however, as with place, it gives us a place to start looking. For example, in many countries there has been a dramatic decline in the incidence of dental caries during the past 25 years, which started with the gradual introduction of fluoride into community water systems and of fluoride rinsing programs in schools and dentists’ offices. Data from New Zealand show that an average 12-year-old in 1971 would have had nine decayed, missing, or filled (DMF) teeth, whereas in 1983 he or she would have had three DMF teeth Fig.1208).
This strongly suggests a preventive role for fluoride because no other factors have been introduced or changed simultaneously on such a massive scale that might otherwise explain the decline of tooth decay. This finding probably also explains why more and more kids and yuppies are wearing braces and having cosmetic dentistry done—dentists have to pay mortgages, too!
The influence of time can also be seen in disorders that occur cyclically or seasonally. We have already discussed the seasonal nature of the black death, which occurred because fleas wanted to escape the cold as much as Canadians. Other disorders that show a cyclical pattern include infectious diseases such as the flu, seasonal affective disorder, and suicide. We might expect suicide to be more prevalent during December or January, when the days are shortest (at least in the northern hemisphere) and the family-oriented holidays exacerbate the loneliness of unattached people, but such is not the case. In fact, as we can see in Fig.1211, suicides actually peak in June for reasons that are not fully understood. In this case the time trend highlights our ignorance of possible causes.
Figure 1208 – (Figure 2-7) Time variable suggests causal relationship between decline in decayed, missing, and filled teeth (DMF) and the use of fluoride in New Zealand
Data from Dublin LI: Suicide: a sociological and statistical study, New York, 1963, Ronald Press.
Some figures may not display clearly when rendered as a PDF or printed.
Figure 1211 – (Figure 2-8) Number of suicides per month per 100,000 people in cities with more than 100,000 inhabitants
Data from Dublin LI: Suicide: a sociological and statistical study, New York, 1963, Ronald Press.
Some figures may not display clearly when rendered as a PDF or printed.
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| 5476. | Streiner DL, Norman GR. PDQ Epidemiology. 2nd ed. Hamilton, Ontario: BC Decker Inc.; 1996. |
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