[MUSIC]. Witches, demons, evil spirits, the wrath of gods, miasmas, or bad air were all once used by people before the modern era of science to explain the cause of disease outbreaks and other calamities. In this lecture, we will talk about modern ways of determining causality. After you have listened to all the lectures for this week, you should be able to complete these learning objectives. They include: define causality and causal inference. State the guidelines for accessing whether an association is casual. Distinguish between real and spurious associations. Describe the nine Bradford Hill Criteria and give examples of each. List other more recent models for understanding causality. In ancient times, people believed that outbreaks and plagues were the result of the will of a god or evil spirits. However, some people wanted to have a more reasonable explanation for these occurrences. People have always tried to give meaning to what they see around them and what affects them. Often, witchcraft was blamed for things such as infant deaths and crop failures as a way to explain their occurrence. The outcome for those accused of witchcraft were witch trials, which most often ended in the death of the accused. For some time, people also believed in miasma, or bad air -- the idea that diseases such as cholera and the Black Death, or the Great Plague were caused by bad air. Over time, the germ theory was developed to explain how some diseases are caused by microorganisms, and the field of epidemiology began with scientific observations of epidemics and other health outcomes. A large part of the field of epidemiology is investigating the causes of disease. A formal definition of causality may be, quote, an event, condition, or characteristic that preceded the outcome or disease event and without which the event either would have not occurred at all or would have not occurred until some later time. End quote. And this is from Rothman and Greenland. American Journal of Public Health, 2005. Causality is not observed, but often inferred. This is known as causal inference. Let's think about what is a causal relationship, and why we care about causality. The primary goal of the epidemiologist is to identify those factors that have a causal impact on disease or health outcome development. For example, the causes of malaria. Determining causal relationships can provide a target for prevention and intervention, such as insecticide treated nets to prevent malaria transmission. It is important to note that sometimes no specific event, condition, or characteristic is sufficient on its own to produce a health outcome or disease. Epidemiologists often use the term risk factor to indicate a factor that is associated with a given health outcome. For example, some risk factors for heart disease include, high blood pressure, a fatty diet, smoking or genetic makeup. If a person has any of these risk factors, they should be regularly monitored by a medical professional. Again, a big part of epidemiology is understanding what causes diseases. So, let's look at some recent headlines as an example of determining causality. What really causes cancer and heart disease? Does one thing such as red meat consumption really cause cancer or heart disease, even when so many other factors may also play a role? For example, what about the role of overall diet, exercise, genetics and stress? Imagine how hard it would be to conduct a randomized controlled trial, to study the effects of eating red meat. Some study participants would be randomized to a very restrictive diet of red meat over a long time period. For another example, let's consider smoking and its link to lung cancer. There are some people due to their genetic makeup or previous experience are susceptible to the effects of smoking, and others who are not susceptible, or as susceptible. These susceptibility factors are part of the causal mechanisms through which smoking may cause lung cancer. Remember, when studying causality, the causation is not observed, but is often inferred based on data and health outcomes. Epidemiologists often employ the counterfactual model. Meaning they ask, what would have been the experience of the exposed if the exposure had not occurred? For example, what would have been the risk of lung cancer if smoking had not occurred? If we determine than an exposure is associated with a health outcome, the next question is whether the observed association reflects a causal relationship. Even if an exposure precedes a health outcome, it does not always mean causality. Even if it is strongly associated. Let's look at a classic example. We can say carrying a lighter is associated with lung cancer. Carrying a lighter precedes lung cancer. So does carrying a lighter cause lung cancer? No. It is important to distinguish between causal associations and spurious associations. When you have a causal association, it means that the occurrence of an event depends upon the occurrence of one or more other events. The event will not happen unless the other events or variables have occurred. When you have a spurious association, it means that bias, failure to control for extraneous variables, such as when there is confounding, misapplied statistics or models, etcetera have played a role. There are a series of criteria that have been developed and refined over the years that now serve as a guideline for causal inference. We will discuss these in another lecture. But the most important point to remember is that causality is not determined by any one factor. Rather, it is a conclusion built on the body of evidence. A cause is something that must proceed the health outcome, and must be necessary for the health outcome to occur. A given health outcome or disease can be caused by more than one causal mechanism, and every causal mechanism involves the joint action of a number of component causes. There are events that directly cause a health outcome, such as being bitten by a mosquito carrying the malaria parasite and contracting malaria. There are also events that indirectly cause a health outcome as part of a larger process, such as the combined role that genetics, smoking, and diet play in developing cancer. It is reasonably safe to say that there are nearly always some genetic and some environmental causes in every causal mechanism. So, why is it important to distinguish between causal and noncausal associations? The reason is we want to know what causes disease or health outcomes, but also causal relationships are used to make public health decisions and design interventions. This concludes our lecture on causality.
