Chapter 14- Deriving inferences from epidemiologic studies

Question 1

Which 2 types of studies depend on nonrandomized observations?

Answer 1

Case-control and cohort studies

Question 2

Natural experiments

Answer 2

When researchers take advantage of groups who have been exposed for non-study purposes, like occupational cohorts in specific industries. Then, the exposed group is compared to an unexposed group

Question 3

Sequence of studies in human populations (5)

Answer 3

1. Clinical observations
2. Available data
3. Case-control studies
4. Cohort studies
5. Randomized trials

Question 4

Clinical observations

Answer 4

When researchers observe an association between variables- like when a surgeon observes that almost all of the lung cancer patients they operate on are smokers

Question 5

Available data

Answer 5

Analyzing routinely available data could provide more information about the research question. Then, new studies (case control and cohort) can be carried out

Question 6

Case-control study

Answer 6

If looking at whether smoking causes lung cancer, the researcher could compare the smoking histories of their patients with lung cancer with those of a group of patients without lung cancer. If the case control study suggests that a certain exposure might be associated with disease, a cohort study might be done next

Question 7

Cohort study

Answer 7

Comparing smokers and nonsmokers and determining the rate of lung cancer in each group or comparing workers exposed to a toxin to workers without the exposure

Question 8

2 step process in carrying out studies

Answer 8

1. We determine whether there is an association or correlation between an exposure or characteristic and the risk of a disease- studies of group or individual characteristics can be done
2. If an association is demonstrated, we determine whether the observed association is likely to be a causal one

Question 9

Real or spurious associations

Answer 9

Poor sample selection could result in a spurious or false association between the variables

Question 10

Confounding variable

Answer 10

A third factor linked to two variables that appear to be related. If a confounding variable is discovered, the relationship isn’t causal

Question 11

Direct causation

Answer 11

A factor directly causes a disease without any intermediate step

Question 12

Indirect causation

Answer 12

A factor causes a disease but only through intermediate steps. In human biology, intermediate steps are almost always present in any causal process

Question 13

Types of causal relationships (4)

Answer 13

1. Necessary and sufficient
2. Necessary but not sufficient
3. Sufficient but not necessary
4. Neither sufficient nor necessary

Question 14

Necessary and sufficient

Answer 14

Without a specific factor, the disease never develops (the factor is necessary), and in the presence of that factor, the disease always develops (the factor is sufficient). This type of relationship rarely occurs. Developing an infectious disease after exposure would represent a necessary and sufficient relationship, although it is more complicated since many other factors influence susceptibility to infection

Question 15

Necessary but not sufficient

Answer 15

This means that multiple factors are required, often in a specific temporal sequence. Carcinogenesis is an example- it is a multistage process involving a promoter acting after an initiator has acted. An initiator or a promoter acting alone will not produce a type of cancer

Question 16

Necessary

Answer 16

Without a specific factor, the disease never develops

Question 17

Sufficient

Answer 17

In the presence of a specific factor, the disease always develops

Question 18

Sufficient but not necessary

Answer 18

In this relationship, the factor alone can produce the disease, but so can other factors acting alone. For example, radiation exposure or benzene exposure can cause leukemia. However, cancer does not develop in everyone who has experienced radiation or benzene exposure

Question 19

Neither sufficient nor necessary

Answer 19

This model is complex and most accurately represents the causal relationships that operate in most chronic diseases. One example is the risk factor clusters for the development of CHD, which generally don’t overlap. Individuals may develop CHD if they are exposed to smoking, diabetes, and low HDL, or a combination of hypercholesterolemia, hypertension, and lack of physical activity, or many other combinations of these factors. Each of these CHD risk factors is neither sufficient nor necessary

Question 20

Analytic epidemiology

Answer 20

In analytic epidemiology, we aim to identify whether exposure to a determinant/factor is a cause of a health-related event or outcome

Question 21

Koch’s postulates (3)

Answer 21

1. The organism is always found with the disease
2. The organism is not found with any other disease
3. The organism, when isolated from one who has the disease and cultured through several generations, produces the disease in animals
   The postulates only apply to infectious diseases, but indicate whether a pathogen and a disease have a causal relationship

Question 22

Guidelines for judging whether an observed association is causal (9)

Answer 22

1. Temporal relationship
2. Strength of the association
3. Dose-response relationship
4. Replication of the findings
5. Biologic plausibility
6. Consideration of alternate explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association

Question 23

Temporal relationship

Answer 23

If a factor is believed to be the cause of a disease, exposure to the factor must occur before disease develops. It’s typically easier to establish a temporal relationship in a prospective cohort. In other studies, exposure information may have be located from past records. The temporal relationship is also important in regard to the length of the interval between exposure and disease- it could take years for the disease to develop

Question 24

Strength of the association

Answer 24

Can be measured by the relative risk or odds ratio. The stronger the association, the more likely it is that the relationship is causal

Question 25

Dose-response relationship

Answer 25

As the dose of exposure increases, the risk of disease also increases. If a dose-response relationship is present, it is strong evidence for a causal relationship. However, it this relationship is absent, it doesn’t rule out a causal relationship. Some diseases have a threshold of exposure that is necessary for the development of disease

Question 26

Replication of the findings

Answer 26

If a relationship is causal, we would expect to find it consistently in different studies and different populations. An observed association should also be seen consistently within subgroups of the population and in different populations

Question 27

Biologic plausibility

Answer 27

Refers to coherence with the current body of biologic knowledge. Epidemiologic observations may precede biological knowledge, but interpreting the meaning of the findings may be difficult if this is the case

Question 28

Consideration of alternate explanations

Answer 28

In judging whether a reported association is causal, the extent to which the investigators have taken other possible explanations into account and the extent to which they have ruled out such explanations are important considerations

Question 29

Cessation of exposure

Answer 29

If a factor is a cause of disease, we would expect the risk of the disease to decline when exposure to the factor is reduced or eliminated. However, in certain cases the disease process may be irreversible

Question 30

Consistency with other knowledge

Answer 30

If a relationship is causal, we would expect the findings to be consistent with other data. Absence of this consistency would not completely rule out this hypothesis, however

Question 31

Specificity of the association

Answer 31

An association is specific when a certain exposure is associated with only one disease- this is the weakest of all the guidelines. Smoking cigarettes causes many diseases- absence of specificity doesn’t negate a causal relationship. Researchers should assess the total pattern of evidence observed before they come to a conclusion

Question 32

What if outcomes were like “pies”?

Answer 32

Each “pie” represents a conceptual scheme for how disease occurs. Each slice of the pie represents a component cause- there can be multiple. Necessary causes would be those that appear in every pie for the disease- it is required for the disease to develop. Sufficient causes are those that can bring about disease (rarely a single factor)