Causal relationship epidemiology and infection

causal relationship epidemiology and infection

A number of models of disease causation have been proposed. Agent originally referred to an infectious microorganism or pathogen: a virus. Now, however, it may be that one can be infected with HIV and never get AIDS, In epidemiology, most causes have much weaker relationships to effects. What approach will you take to study the relationship between exposure and effect? Is the association “real,” i.e. causal? Infectious Disease Epidemiology .

Only very few diseases could then have a causea A cause is a condition with which the effect must occur. Again, only few diseases could then have a causeb A cause is made up of several components, no single one of which is sufficient of its own, which taken together must lead to the effect.

Introduces unnecessary complexity in cases of simple dose response and in cases of interaction between components A cause is a condition that increases the probability of occurrence of the effect. Is, in the strict sense, unprovable because there is only one world and one cannot observe it twice—once with and once without the condition Open in a separate window aMany disease definitions already include a cause e.

causal relationship epidemiology and infection

Except for injuries due to extreme physical or chemical conditions and exposure to extremely contagious infectious agents that lead to death e. How, then, should cause and causation be defined? However, all of these definitions summarized in Table 1 have severe deficits.

Not totally unexpected, the definitions found in the literature are insufficient to provide a basis for the notion of disease causation. As pointed out above for physical phenomena, it is also impossible for disease processes to draw an ontologic demarcation within the indefinite stream of events between causal and noncausal associations. Consider a human being as a complex input—output system that is described by a path through a state space of likely very high dimensionality that may or may not explicitly depend on time.

The task is to solve the equations that relate the input stream, the output stream, and the internal states to each other. If we were in possession of such a tool, we would not need the crutch of a concept of causation.

Meanwhile, in a pragmatic sense, it is reasonable to stay with this concept but hold in mind that it is just an economical way to organize the otherwise unfathomable stream of events and to take the necessary steps to counteract or prevent the disease process.

The process of diagnosis itself is one of abstraction and generalization because no two diseased human beings given the same diagnosis have exactly the same features. In this pragmatic sense, disease cause can be defined as follows: Given two or more populations of subjects that are sufficiently similar for the problem under study, a disease cause is a set of mutually exclusive conditions by which these populations differ that increase the probability of the disease.

In some cases, the similarity must be high, such that only homozygous twins can be studied; in other cases, maybe only sex and age must be considered, or the state of immunity. Hence, this temporal relation is a precondition for an agent to be considered a causal factor.

This definition is in line with the main designs of epidemiologic studies: It is also in line with the pragmatic definition that assessment of causality affords more than just the observation of an increased incidence or prevalence in some group or the other. Taking Refuge in Causality It seems that the first time causality entered the discussion on epidemiologic results was during the tobacco controversy in the late s and early s. In particular, the criticism of Fisher concerning the conclusions drawn from the British Doctors Study by Doll and Bradford Hill initiated a detailed consideration of the concept of causality that led to the famous presidential address by Bradford Hill to the Section of Occupational Medicine of the Royal Society of Medicine in In this talk, Bradford Hill discussed nine issues that should be addressed when deciding whether an observed association is a causal relationship.

The Bradford Hill criteria were established such that, in the case they are met for a specific factor, this would increase our confidence in this factor being causally related to the disease.

However, they were not intended to dismiss a factor as potentially causing the disease: First, one has to discriminate between evidence for no causal relationship, and no evidence of a causal relationship Altman and Bland The former expresses an important piece of evidence that may have substantial consequences on steps taken to prevent health hazards, whereas the latter simply expresses lack of knowledge.

It is, however, often misunderstood as an exculpation of the agent in question and is readily misused by interested parties to claim that exposure is not associated with adverse health effects. Some examples of such statements illustrate the point: There are significant differences between these statements. Hence, it points mainly to the lack of knowledge accumulated so far. The second one goes a step further: It claims that risk assessment based on the weight-of-evidence approach [as applied by the U.

1.4b Causal models

Environmental Protection Agency U. However, there is no category of this type in the weight-of-evidence approaches.

causal relationship epidemiology and infection

Because of the by far higher demands on quality and size of studies set out to dismiss the assumption of carcinogenicity, there is an inherent imbalance of classification concerning carcinogenicity and lack of carcinogenicity. The first statement goes still further: It claims that an analysis based on the Bradford Hill criteria confirms that there is no causal relationship.

All other evidence may reduce the weight in favor of a causal relationship but cannot confirm that there is no causal relationship. Are There Criteria for Causation? It is a complete misinterpretation of the nine issues considered by Bradford Hill that they can be a type of checklist to establish causation.

Causality and the Interpretation of Epidemiologic Evidence

But it may turn out that they owe their popularity, still persisting after 40 years, exactly to this misconception. Because the definition of a disease cause given above affords the existence of mutually exclusive conditions, in a strict sense, causation can be indicated only by experimental production and control of all relevant conditions. This, however, leads to ethical problems if the factor is potentially debilitating or lethal. And it is practically impossible if the latency is long, as it is for chronic diseases.

Resorting to animal experimentation can reduce some of these problems but introduces new ones, because inference from results in animals to effects in humans is far from trivial.

Hence, we are often left with a number of problems that cannot be optimally solved, and therefore there is no set of criteria that, if fulfilled, would result in attributing a factor as either causally related or not. This does not mean that we cannot, to the best of our present knowledge, come to a decision concerning the relationship of an agent and a disease. Or, as Bradford Hill said 40 years ago: All scientific work is incomplete—whether it be observational or experimental.

All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time. A Pragmatic Approach Concerning a particular chemical or physical factor, general medical knowledge may suffice to attribute it as harmful and as causing illness or death but even in extreme cases such derivations may not be altogether valid—e.

Everest without respiratory aid. So we are dealing with either less obvious hazards or those that occur only rarely or in a small proportion of the population. The evidence may stem from all kinds of sources, but often we start only from the pessimistic assumption that an agent either not present in the natural environment or present only at much lower levels may be harmful to health. Or it may be that during routine surveillance, a high prevalence of a rare disease is observed that coincides with a rare environmental condition.

How should we come to a conclusion whether the suspected environmental condition is causing disease? It might be worthwhile to stress that there are cases where we do not need the verdict of causation before we take action e. Starting from the definition of a disease cause stated above, it is obvious that three main issues need to be addressed to simplify the discussion, let us speak of the set of exclusive conditions as of an agent or determinant A: Is the probability of the disease conditional on the presence of A higher than in the absence of A?

Association Although we can to some degree rely on statistical decision theory concerning an observed difference, some problems need to be addressed: First, there are cases where we observe an incidence only in those exposed to A and contrast it to the overall incidence in the population as was the case with hepatic angiosarcoma in workers exposed to vinyl chloride monomer. If the disease is extremely rare in the population, it may not be feasible to do a conventional epidemiologic study.

However, if a plausible mechanism of action can be delineated, the observation of an unexpectedly high incidence of the disease may suffice for a verdict of causation. Second, in the case—control approach, we estimate not the conditional probabilities of the disease but their ratio. Furthermore, it is questionable whether statistical decision theory based on random sampling can be applied without further consideration.

Typically, all cases of the target disease occurring within a specified region or even only those diagnosed in one or several hospitals and during a specified period of time are intentionally included, and only controls are sampled either from the population or from hospital cases presenting with other than the target disease.

Furthermore, the population from which the cases and controls originate has, in general, not been stable during the relevant past.

Cases of the target disease that occurred before study onset are not included, and also migration in and out of the target area may play an important role, as might deaths from other and maybe related causes.

Because of these circumstances and the additional problem of reliably assessing the presence of A retrospectively, case—control studies are often denied the potential to form the basis of a causal interpretation.

However, this is exaggerating the difficulties associated with this study type. Especially if several case—control studies from different areas and time periods are available, a generalization about the ratio of incidences can be made if the different sources of bias have been thoroughly addressed.

causal relationship epidemiology and infection

Finally, even if the relative risk whether estimated from rate ratios, odds ratios, or hazard ratios is high, statistical significance may not be reached if the number of cases exposed to A is low.

Environmental equivalence Ideally, those exposed to A should share the same conditions, besides A, with those not exposed to A. If not, all relevant conditions that are potentially related to both A and the outcome i. Failing to do so—that is, controlling for some but not others—may increase confounding instead of removing it e.

Because the number of potentially confounding factors is indefinite and judgment about the degree of similarity between environmental conditions depends on limited experience, there is always the possibility that an observed association is due to confounding.

On the other hand, the mere suspicion that an observed association is due to confounding does not conform to scientific reasoning because it cannot be refuted by a finite sequence of empirical tests.

Analysis of uncontrolled confounding Greenland ; Robins et al. These approaches may replace the earlier procedures, as already applied by Bradford Hill. Different diseases require different balances and interactions of these three components. Development of appropriate, practical, and effective public health measures to control or prevent disease usually requires assessment of all three components and their interactions.

Generally, the agent must be present for disease to occur; however, presence of that agent alone is not always sufficient to cause disease.

A variety of factors influence whether exposure to an organism will result in disease, including the organism's pathogenicity ability to cause disease and dose. Over time, the concept of agent has been broadened to include chemical and physical causes of disease or injury.

causal relationship epidemiology and infection

These include chemical contaminants such as the L-tryptophan contaminant responsible for eosinophilia-myalgia syndromeas well as physical forces such as repetitive mechanical forces associated with carpal tunnel syndrome.

While the epidemiologic triad serves as a useful model for many diseases, it has proven inadequate for cardiovascular disease, cancer, and other diseases that appear to have multiple contributing causes without a single necessary one. Host refers to the human who can get the disease. A variety of factors intrinsic to the host, sometimes called risk factors, can influence an individual's exposure, susceptibility, or response to a causative agent.

Opportunities for exposure are often influenced by behaviors such as sexual practices, hygiene, and other personal choices as well as by age and sex. Susceptibility and response to an agent are influenced by factors such as genetic composition, nutritional and immunologic status, anatomic structure, presence of disease or medications, and psychological makeup.

Causality and the Interpretation of Epidemiologic Evidence

Environment refers to extrinsic factors that affect the agent and the opportunity for exposure. Environmental factors include physical factors such as geology and climate, biologic factors such as insects that transmit the agent, and socioeconomic factors such as crowding, sanitation, and the availability of health services. Component causes and causal pies Because the agent-host-environment model did not work well for many non-infectious diseases, several other models that attempt to account for the multifactorial nature of causation have been proposed.

One such model was proposed by Rothman inand has come to be known as the Causal Pies. An individual factor that contributes to cause disease is shown as a piece of a pie.

Principles of Epidemiology | Lesson 1 - Section 8

After all the pieces of a pie fall into place, the pie is complete — and disease occurs. The individual factors are called component causes. The complete pie, which might be considered a causal pathway, is called a sufficient cause.

A disease may have more than one sufficient cause, with each sufficient cause being composed of several component causes that may or may not overlap. A component that appears in every pie or pathway is called a necessary cause, because without it, disease does not occur.

Note in Figure 1. Am J Epidemiol ; The component causes may include intrinsic host factors as well as the agent and the environmental factors of the agent-host-environment triad.