Case Report and Case Control
A case report is documentation of a single novel or interesting case. This represents the lowest level of evidence, since it only represents one instance. If you put several case reports together, you have a case series. With this group of cases, you can look for patterns and start to generate hypotheses. One important limitation to both of these is that there is no control group. You cannot use case reports or case series to determine if a particular exposure is causing a particular outcome. But it can get you to start thinking.
The case series can also be useful in generating a case definition. This is helpful in identifying the patients that can be subjects for future studies. Once we have the definition, we can compare those patients who have the disease (the cases) and those who don’t to determine what is different between the two. This allows us to perform studies which include control groups. Case control studies can be used to generate hypotheses. In the video, we describe a case control study looking at HIV and inhalants.
Ecologic Studies
The key to ecologic studies is that the measures are characteristics of groups rather than individuals. And these changes are followed across time (rather than at one particular point in time). So instead of looking at hypertension a particular people, you may be looking hypertension in a particular country (group of people). The example given in the video showed the prevalence of hypertension in different countries.
Strengths of ecologic studies are that they are fast and cheap, as they can use information that is already available. Limitations include the fact that they cannot control for effects of confounding factors. There is also no information on the level of exposure, only that there was an exposure. Another limitation is the ecological fallacy (aggregation bias): the assumption that relations that apply at a group level also apply at an individual level. More on that in the next video.
Ecologic Studies & Ecologic Fallacy
The ecological fallacy is the assumption that associations that apply at a group level also apply at an individual level. The group level data may mask relationships that exist at an individual level. They may actually even present the opposite.
In medicine, we are dealing with individual patients and so are most interested at these relationships at the individual level.
Cross Sectional Studies
One step higher on the evidence hierarchy above the ecological study. It is also known as a prevalence study, but looks at individuals. They look at individuals at a snapshot in time and ask does this person have the disease and the exposure. These data can be presented in a 2×2 table with disease/no-disease across the top and exposure/no-exposure across the bottom.
In the example given, the prevalence of diabetes was higher in obese than non-obese patients. This suggests a relationship. This doesn’t imply causation, though. Diabetes can be causing obesity, obesity can be causing diabetes, or some third variable can be causing both. These studies only show correlations, and so are often called correlational studies. These generated hypotheses are good to test in other studies. These studies are also susceptible to prevalence-incidence bias. What’s that you ask?
Cross-Sectional Studies & Prevalence Incidence Bias
Cross-sectional studies look at prevalence of disease. You’ll remember that prevalence is made up of incidence and duration. So when the prevalence of a disease is higher in the exposed group, is that because the exposure increased the incidence of the disease (made more people get it) or the duration (made the disease last longer)? So we are often more worried about increasing the incidence of the disease. Does obesity increase the incidence of diabetes?
Prevalence = Incidence x Duration
What if obesity increased the survival of diabetics? That’s not necessarily a bad thing, but the prevalence would be higher. What if obesity decreased the survival of diabetics? Then would be a bad thing, but the prevalence would be lower making it look like obesity decreases the risk of diabetes.
Additionally, prevalence data misses patients who get the disease AFTER the start of the study and die BEFORE the end of the study. These sicker patients are therefore excluded.
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