Discussion: Epidemiology and nursing profession

• Avoiding Epidemiologic Traps Ecologic Fallacy The ecologic fallacy, simply stated, is the error made when one makes incorrect inferences about an individual or small group’s probability of having a certain characteristic, based on the probability of that characteristic in the population from which that individual or small group comes. Let’s look at an example: Ecologic Fallacy Let’s say that we know that in a University with lower and upper campuses, that because upper campus houses graduate and honors programs, that the average GPA of students on upper campus is higher. Does it follow that in a particular introductory psychology class offered to students from across the campus, if the professor tabulates the GPA of students enrolling in that class, that the ones from upper campus will have a higher GPA than those from lower campus? Ecologic Fallacy  Not necessarily!  The mistake the psychology professor would make with such an assumption would be to substitute risk at the population level for risk at the individual level. The population-level difference in GPA, in this case, is attributable to the influence of students who probably are not going to take introductory psychology. Discussion: Epidemiology and nursing profession

Confounding Consider the following: 7 of 110 women, and 17 of 100 men, are positive for a new viral antibody “N” on when screened with a blood test. The odds ratio is 3.01, easily significant with a chi-square test. However … look what happens when the data are analyzed by who has or has not ever lived outside the U.S.: Confounding Among those having lived outside the U.S., 15/50 men and 3/10 women (both 30%) have the N antibody. Among those never having lived outside the U.S., 2/50 men and 4/100 women have N antibody (both 4%). The odds ratio in each group is 1.00 – there is actually no relationship at all between gender and having the N antibody. It only appeared so because of a statistical fluke. Confounding This is an example of what is known as confounding. The relationship between gender and the N antibody is confounded by whether the person has lived outside the U.S. Confounding occurs when the relationship between “A” and “C” is distorted (in either direction) by “B”, which is associated with “A”, and, independent of its relationship with A, is associated with “C”. Bias Confounding is an illustration of what is known as “bias” – that is, a situation where an unrelated factor obscures the true outcome. Discussion: Epidemiology and nursing profession

Bias can come from a number of sources: four of the most common are recall bias, nonresponse bias, selection bias, and publication bias. Selection Bias If, for instance, hospital workers are surveyed for their opinions about the medical care system and their responses considered representative of the population at large, the result will be inaccurate as the result of selection bias. Hospital workers are likely to have opinions about medical care for reasons that would not be applicable to most people. Recall Bias When, for instance, persons with a specific illness are surveyed about their prior exposures to possible risk factors, and their responses compared to those who are not ill, investigators must consider that the experience of facing illness may make a person much more likely to have thought about and to recall prior experiences. This is an example of recall bias. Recall bias is a particular problem in case-control studies. Nonresponse Bias When investigators are trying to assess the opinions of a group of people, and a significant fraction of the group chooses not to respond (call back, fill out the survey, log in, etc.), it is quite likely that those who do respond are doing so because they have an interest in the topic which influences their opinions in a way not typical of the others. This is an example of nonresponse bias. Publication Bias  Here is one that people sometimes do not think about: it is simply that studies with positive or remarkable findings are more likely to be published in journals, while studies that find no association are less likely to see the light of day. Over time, this can give the impression that certain associations are stronger than they really are. Discussion: Epidemiology and nursing profession

 Be careful with this one – not every rejection of an article is due to bias! Sometimes the editors discover another form of bias. Effect Modification Consider the following: 100 of 200 (50%) men, and 140 of 200 (70%) women are found to have antibody “R” on blood testing. Clearly, the relationship is significant. However … look what happens when the data are analyzed by whether the men or women take multivitamins regularly: Effect Modification In the group that does not take vitamins, 50/100 men and 50/100 women have the R antibody (both 50%). Discussion: Epidemiology and nursing profession

In the group that does take vitamins, 50/100 men and 90/100 women have the antibody! Clearly, the relationship between gender and the R antibody is very different depending on whether the person takes vitamins. Effect Modification This phenomenon is known as “effect modification”. We say that vitamins modify the effect of gender on R antibody status. Women (and not men) are susceptible to “R” in some way that requires the presence of a vitamin to develop the antibody. The relationship between gender and R is nonetheless real, but it cannot be understood properly unless one considers the effect of the vitamin. … Discussion: Epidemiology and nursing profession