Risk: Exposure to Disease
From the study of the characteristics of persons who later develop coronary heart disease (CHD) and comparison with the characteristics of those who remain free of this disease, it is possible many years before any overt symptoms or signs become manifest… to put together a profile of those persons in whom there is a high risk of developing CHD … It has seldom been possible in noninfectious disease to identify such highly susceptible individuals years before the development of disease.
—Thomas Dawber and William Kannel 1961
KEY WORDS
Observational study
Cohort study
Cohort
Exposed group
Unexposed group
Incidence study
Prospective cohort study
Retrospective/historical cohort study
Case-cohort design
Measure of effect
Absolute risk
Attributable risk
Risk difference
Relative risk
Risk ratio
Population-attributable risk
Population-attributable fraction
Extraneous variable
Covariate
Crude measure of effect
Confounding
Intermediate outcome
Confounding variable/Confounder
Controlling
Restriction
Matching
Stratification
Standardization
Multivariable analysis
Logistic regression
Cox proportional hazard
Unmeasured confounder
Residual confounding
Effect modification
Interaction
STUDIES OF RISK
This chapter describes how investigators obtain estimates of risk by observing the relationship between exposure to possible risk factors and the subsequent incidence of disease. It describes methods used to determine risk by following groups into the future and also discusses several ways of comparing risks as they affect individuals and populations. Chapter 6 describes methods of studying risk by looking backward in time.
The most powerful way to determine whether exposure to a potential risk factor results in an increased risk of disease is to conduct an experiment in which the researcher determines who is exposed. People currently without disease are divided into groups of equal susceptibility to the disease in question. One group is exposed to the purported risk factor and the other is not, but the groups otherwise are treated the same. Later, any difference in observed rates of disease in the groups can be attributed to the risk factor. Experiments are discussed in Chapter 9.
When Experiments Are Not Possible or Ethical
The effects of most risk factors in humans cannot be studied with experimental studies. Consider some of the risk questions that concern us today: Are inactive people at increased risk for cardiovascular disease, everything else being equal? Do cellular phones cause brain cancer? Does obesity increase the risk of cancer? For such questions, it is usually not possible to conduct an experiment. First, it would be unethical
to impose possible risk factors on a group of healthy people for the purposes of scientific research. Second, most people would balk at having their diets and behaviors constrained by others for long periods of time. Finally, the experiment would have to go on for many years, which is difficult and expensive. As a result, it is usually necessary to study risk in less obtrusive ways.
to impose possible risk factors on a group of healthy people for the purposes of scientific research. Second, most people would balk at having their diets and behaviors constrained by others for long periods of time. Finally, the experiment would have to go on for many years, which is difficult and expensive. As a result, it is usually necessary to study risk in less obtrusive ways.
Clinical studies in which the researcher gathers data by simply observing events as they happen, without playing an active part in what takes place, are called observational studies. Most studies of risk are observational studies and are either cohort studies, described in the rest of this chapter, or case-control studies, described in Chapter 6.
Cohorts
As defined in Chapter 2, the term cohort is used to describe a group of people who have something in common when they are first assembled and who are then observed for a period of time to see what happens to them. Table 5.1 lists some of the ways in which cohorts are used in clinical research. Whatever members of a cohort have in common, observations of them should fulfill three criteria if the observations are to provide sound information about risk of disease.
Table 5.1 Cohorts and Their Purposes | |||||||||||||||||||||
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They do not have the disease (or outcome) in question at the time they are assembled.
They should be observed over a meaningful period of time in the natural history of the disease in question so that there will be sufficient time for the risk to be expressed. For example, if one wanted to learn whether neck irradiation during childhood results in thyroid neoplasms, a 5-year follow-up would not be a fair test of this hypothesis, because the usual time period between radiation exposure and the onset of disease is considerably longer.
All members of the cohort should be observed over the full period of follow-up or methods must be used to account for dropouts. To the extent that people drop out of the study and their reasons for dropping out are related in some way to the outcome, the information provided by an incomplete cohort can misrepresent the true state of affairs.
Cohort Studies
The basic design of a cohort study is illustrated in Figure 5.1. A group of people (a cohort) is assembled, none of whom has experienced the outcome of interest, but all of whom could experience it. (For example, in a study of risk factors for endometrial cancer, each member of the cohort should have an intact uterus.) Upon entry into the study, people in the cohort are classified according to those characteristics (possible risk factors) that might be related to outcome. For each possible risk factor, members of the cohort are classified either as exposed (i.e., possessing the factor in question, such as hypertension) or unexposed. All the members of the cohort are then observed over time to see which of them experience the outcome, say, cardiovascular disease, and the rates of the outcome events are compared in the exposed and unexposed groups. It is then possible to see whether potential risk factors are related to subsequent outcome events. Other names for cohort studies are incidence studies, which emphasize that patients are followed over time; prospective studies, which imply the forward direction in which the patients are pursued; and longitudinal studies, which call attention to the basic measure of new disease events over time.
The following is a description of a classic cohort study that has made important contributions to our understanding of cardiovascular disease risk factors and to modern methods of conducting cohort studies.
 Figure 5.1 ▪ Design of a cohort study of risk. Persons without disease are divided into two groups—those exposed to a risk factor and those not exposed. Both groups are followed over time to determine what proportion of each group develops disease. |
Example
The Framingham Study (1) was begun in 1949 to identify factors associated with an increased risk of coronary heart disease (CHD). A representative sample of 5,209 men and women, aged 30 to 59 years, was selected from approximately 10,000 persons of that age living in Framingham, a small town near Boston. Of these, 5,127 were free of CHD when first examined and, therefore, were at risk of developing CHD. These people were re-examined biennially for evidence of coronary disease. The study ran for 30 years and now continues as the Framingham Offspring Study (2). It demonstrated that the risk of developing CHD is associated with elevated blood pressure, high serum cholesterol, cigarette smoking, glucose intolerance, and left ventricular hypertrophy. There was a large difference in risk of CHD between those with none and those with all of these risk factors. Combining the risk factors identified in this study gave rise to one of the most often used risk prediction tools in clinical medicine—the Framingham Risk Score for cardiovascular disease.
Prospective and Historical Cohort Studies
Cohort studies can be conducted in two ways (Fig. 5.2). The cohort can be assembled in the present and followed into the future (a prospective cohort study), or it can be identified from past records and followed forward from that time up to the present (a retrospective cohort study or a historical cohort study). The Framingham Study an example of a prospective cohort study. Useful retrospective cohort studies are appearing increasingly in the medical literature because of the availability of large computerized medical databases.
Prospective Cohort Studies
Prospective cohort studies can assess purported risk factors not usually captured in medical records, including many health behaviors, educational level, and socioeconomic status, which have been found to have important health effects. When the study is planned before data are collected, researchers can be sure to collect information about possible confounders. Finally, all the information in a prospective cohort study can be collected in a standardized manner that decreases measurement bias.
 Figure 5.2 ▪ Retrospective and prospective cohort studies. Prospective cohorts are assembled in the present and followed forward into the future. In contrast, retrospective cohorts are made by going back into the past and assembling the cohort, for example, from medical records, then following the group forward to the present. |
Example
How much leisure time physical activity is needed to achieve health benefits? Several guidelines suggest a minimum of 30 minutes a day for 5 days a week, but most people do not follow the recommendation. Can less physical activity achieve health benefits? A prospective cohort study was undertaken among more than 415,000 adults who answered a standard questionnaire about their physical activity and were followed for an average of 8 years (3). Results showed that increasing amounts of leisure activity were correlated with reduced all-cause mortality and longer life expectancy compared to those who reported no activity. As little as 15 minutes of activity per day correlated with a decreased mortality of 14% and an increased life expectancy of 3 years, even when accounting for education level, physical labor at work, and other health conditions.
Historical Cohort Studies Using Medical Databases
Historical cohort studies can take advantage of computerized medical databases and population registries that are used primarily for patient care or to track population health. The major advantages of historical cohort studies over classical prospective cohort studies are that they take less time, are less expensive, and are much easier to do. However, they cannot undertake studies of factors not recorded in computerized databases, so patients’ lifestyle, social standing, education, and other important health determinants usually cannot be included in the studies. Also, information in many databases, especially medical care information, is not collected in a standardized manner, leading to the possibility of bias in results. Large computerized databases are particularly useful for studying possible risk factors and health outcomes that are likely to be recorded in medical databases in somewhat standard ways, such as diagnoses and treatments.
Example
The incidence of autism increased sharply in the 1990s, coinciding with an increasing vaccination of young children for measles, mumps, and rubella (MMR). A report linking MMR vaccination and autism in several children caused widespread alarm that vaccination (or the vaccine preservative, thimerosal) was responsible for the increasing incidence of autism. In some countries, MMR vaccination rates among young children dropped, resulting in new outbreaks and even deaths from measles. Because of the seriousness of the situation, several studies were undertaken to evaluate MMR vaccine as a possible risk factor. In Denmark, a retrospective cohort study included all children (537,303) born from January 1991 through December 1998 (4). The investigators reviewed the children’s countrywide health records and determined that 82% received the MMR vaccine (physicians
must report vaccinations to the government in order to receive payment); 316 children were diagnosed with autism, and another 422 with autistic-spectrum disorders. The frequency of autism among children who had been vaccinated was similar (in fact, slightly less) to that among children not receiving MMR vaccine. This, along with other studies, provided strong evidence against the suggestion that MMR vaccine causes autism. Subsequently, the original study leading to alarm was investigated for fraud and conflict of interest and was retracted by The Lancet in 2010 (5).
must report vaccinations to the government in order to receive payment); 316 children were diagnosed with autism, and another 422 with autistic-spectrum disorders. The frequency of autism among children who had been vaccinated was similar (in fact, slightly less) to that among children not receiving MMR vaccine. This, along with other studies, provided strong evidence against the suggestion that MMR vaccine causes autism. Subsequently, the original study leading to alarm was investigated for fraud and conflict of interest and was retracted by The Lancet in 2010 (5).
Case-Cohort Studies
Another method using computerized medical databases in cohort studies is the case-cohort design. Conceptually, it is a modification of the retrospective cohort design that takes advantage of the ability to determine the frequency of a given medical condition in a large group of people. In a case-cohort study, all exposed people in a cohort, but only a small random sample of unexposed people are included in the study and followed for some outcome of interest. For efficiency, the group of unexposed people is “enriched” with all those who subsequently suffer the outcome of interest (i.e., become cases). The results are then adjusted to reflect the sampling fractions used to obtain the sample. This efficient approach to a cohort study requires that frequencies of outcomes be determined in the entire group of unexposed people; thus, the need for a large, computerized, medical database.
Example
Does prophylactic mastectomy protect women who are at increased risk for breast cancer? A case-cohort study was done to examine this question in six health maintenance organizations, all of which had computerized databases of diagnoses and surgical procedures on their members. Investigators identified all 276 women who underwent bilateral prophylactic mastectomy over a number of years and followed them forward over time to determine if they developed breast cancer. For the comparison group, the investigators randomly sampled a similar group of women not undergoing the procedure and enriched the sample with women who subsequently developed breast cancer. “Enrichment” was accomplished by knowing who among 666,800 eligible women developed breast cancer—through examination of the computerized database. The investigators randomly sampled about 1% of comparison women of a certain age who developed breast cancer,† but only about .01% of women who did not. Adjustments for the sampling fractions were then made during the analysis. The results showed that bilateral prophylactic mastectomy was associated with a 99% reduction in breast cancer among women at higher risk (6).
Advantages and Disadvantages of Cohort Studies
Well-conducted cohort studies of risk, regardless of type, are the best available substitutes for a true experiment when experimentation is not possible. They follow the same logic as a clinical trial and allow measurement of exposure to a possible risk factor while avoiding any possibility of bias that might occur if exposure were determined after the outcome was already known. The most important scientific disadvantage of cohort studies (in fact, all observational studies) is that they are subject to a great many more potential biases than are experiments. People who are exposed to a certain risk factor in the natural course of events are likely to differ in a great many ways from a comparison group of people not exposed to the factor. If some of these other differences are also related to the disease in question, they could confound any association observed between the putative risk factor and the disease.
The uses, strengths, and limitations of the different types of cohort studies are summarized in Table 5.2. Several of the advantages and disadvantages apply regardless of type. However, the potential
for difficulties with the quality of data is different for the three. In prospective studies, data can be collected specifically for the purposes of the study and with full anticipation of what is needed. It is thereby possible to avoid measurement biases and some of the confounders that might undermine the accuracy of the results. However, data for historical cohorts are usually gathered for other purposes—often as part of medical records for patient care. Except for carefully selected questions, such as the relationship between vaccination and autism, the data in historical cohort studies may not be of sufficient quality for rigorous research.
for difficulties with the quality of data is different for the three. In prospective studies, data can be collected specifically for the purposes of the study and with full anticipation of what is needed. It is thereby possible to avoid measurement biases and some of the confounders that might undermine the accuracy of the results. However, data for historical cohorts are usually gathered for other purposes—often as part of medical records for patient care. Except for carefully selected questions, such as the relationship between vaccination and autism, the data in historical cohort studies may not be of sufficient quality for rigorous research.
Table 5.2 Advantages and Disadvantages of Cohort Studies | ||||||||||||||||||||||||||||||||||||
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Prospective cohort studies can also collect data on lifestyle and other characteristics that might influence the results, and they can do so in standard ways. Many of these characteristics are not routinely available in retrospective and case-cohort studies, and those that are usually are not collected in standard ways.
The principal disadvantage of prospective cohort studies is that when the outcome is infrequent, which is usually so in studies of risk, a large number of people must be entered in a study and remain under observation for a long time before results are available. Having to measure exposure in many people and then follow them for years is inefficient when few ultimately develop the disease. For example, the Framingham Study of cardiovascular disease (the most common cause of death in America) was the largest study of its kind when it began. Nevertheless, more than 5,000 people had to be followed for several years before the first, preliminary conclusions could be published. Only 5% of the people had experienced a coronary event during the first 8 years. Retrospective and case-cohort studies get around the problem of time but often sacrifice access to important and standardized data.
Another problem with prospective cohort studies results from the people under study usually being “free living” and not under the control of researchers. A great deal of effort and money must be expended to keep track of them. Prospective cohort studies of risk, therefore, are expensive, usually costing many millions, sometimes hundreds of millions, of dollars.
Because of the time and money required for prospective cohort studies, this approach cannot be used for all clinical questions about risk, which was a major reason for efforts to find more efficient, yet dependable, ways of assessing risk, such as retrospective and case-cohort designs. Another method, case-control studies, is discussed in Chapter 6.
Table 5.3 Measures of Effect | |||||||||||||||||||||
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WAYS TO EXPRESS AND COMPARE RISK
The basic expression of risk is incidence, which is defined in Chapter 2 as the number of new cases of disease arising during a given period of time in a defined population that is initially free of the condition. In cohort studies, the incidence of disease is compared in two or more groups that differ in exposure to a possible risk factor. To compare risks, several measures of the association between exposure and disease, called measures of effect, are commonly used. These measures represent different concepts of risk, elicit different impressions of the magnitude of a risk, and are used for different purposes. Four measures of effect are discussed in the following text. Table 5.3 summarizes the four, along with absolute risk, and Table 5.4 demonstrates their use with the risk of lung cancer among smokers and non-smokers.
Absolute Risk
Absolute risk is the probability of an event in a population under study. Its value is the same as that for incidence, and the terms are often used interchangeably. Absolute risk is the best way for individual patients and clinicians to understand how risk factors may affect their lives. Thus, as Table 5.4 shows, although smoking greatly increases the chances of dying from lung cancer, among smokers the absolute risk of dying from lung cancer each year in the population studied was 341.3 per
100,000 (3 to 4 lung cancer deaths per 1,000 smokers per year).
100,000 (3 to 4 lung cancer deaths per 1,000 smokers per year).
Table 5.4 Calculating Measures of Effect: Cigarette Smoking and Death from Lung Cancer in Mena
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