Читать книгу Statistics in Nutrition and Dietetics - Michael Nelson - Страница 47

A key feature of epidemiological studies is the attention paid to confounding factors and bias. Confounding factors are associated with both the exposure and the outcome. Suppose that we were comparing a group of cases who had recently had their first heart attack with a group of controls who had never had a heart attack. Say that we were interested in knowing if higher oily fish consumption was associated with lower rates of heart attack. We could measure oily fish consumption using a food frequency questionnaire that asked about usual diet. Suppose we found that it was lower among the cases. Is this good evidence that higher oily fish consumption is protective against having a heart attack? What if the cases, on average, were 10 years older than the controls, and that younger people tended to have higher levels of oily fish in their diet. This could explain the apparent association of higher oily fish consumption with decreased risk of heart attack. In this case, age would be referred to as a confounding factor. Confounding factors need to be associated with both the exposure and the outcome that we are interested in. We could match our cases with controls of the same age. Alternatively, we could use a statistical approach that took age into account in the analysis. The most common confounding factors – things like age, gender, social class, and education – need to be controlled for when comparing one group with another. Other factors such as smoking, disease status (e.g. diabetes), or body mass index (BMI) may also be taken into account, but these may be explanatory factors or factors in the causal pathway rather than true confounders.

Bias is a problem associated with measuring instruments or interviewers. Systematic bias occurs when everyone is measured with an instrument that always gives an answer that is too high or too low (like an inaccurate weighing machine). Bias can be constant (every measurement is inaccurate by the same amount) and or proportional (the size of the error is proportional to the size of the measurement, e.g. the more you weigh the greater the inaccuracy in the measurement). Bias is a factor that can affect any study and should be carefully controlled.

Some types of bias may simply reduce our ability to detect associations between exposure and outcome. This is ‘noise in the system’. It means that there may be an association between exposure and outcome, but our data are too ‘noisy’ for us to be able to detect it. For example, we know that there is day‐to‐day and week‐to‐week variation in food and drink consumption. We need to try and collect sufficient information to be able to classify subjects according to their ‘usual’ consumption.

Other types of bias mean that the information that we obtain is influenced by the respondent’s ability to give us accurate information. Subjects who are overweight or obese, for example, or who have higher levels of dietary restraint, tend to under‐report their overall food consumption, especially things like confectionery or foods perceived as ‘fatty’. Subjects who are more health‐conscious may over‐report their fruit and vegetable consumption because they regard these foods as ‘healthy’ and want to make a good impression on the interviewer. In these instances, making comparisons between groups becomes problematic because the amount of bias is related to the type of individual which may in turn be related to their disease risk.

Dealing with issues such as confounding, residual confounding, factors in the causal pathway, and different types of bias are fully addressed in epidemiological textbooks [9, 10].