Errors in exposure measurement may have different impacts on the exposure-disease relationship being studied, depending on how the errors are distributed. If an epidemiological study has been conducted blindly (i.e., measurements have been taken with no knowledge of the disease or health status of the study participants) we expect that measurement error will be evenly distributed across the strata of disease or health status.

Table 1 provides an example: suppose we recruit a cohort of people exposed at work to a toxicant, in order to investigate a frequent disease. We determine the exposure status only at recruitment (T₀), and not at any further points in time during follow-up. However, let us say that a number of individuals do, in fact, change their exposure status in the following year: at time T₁, 250 of the original 1,200 exposed people have ceased being exposed, while 150 of the original 750 non-exposed people have started to be exposed to the toxicant. Therefore, at time T₁, 1,100 individuals are exposed and 850 are not exposed. As a consequence, we have “misclassification” of exposure, based on our initial measurement of exposure status at time T₀. These individuals are then traced after 20 years (at time T₂) and the cumulative risk of disease is evaluated. (The assumption being made in the example is that only exposure of more than one year is a concern.)

Table 1. Hypothetical cohort of 1950 individuals (exposed and unexposed at work), recruited at time T₀ and whose disease status is ascertained at time T₂

Exposed workers                                1200      250 quit exposure       1100 (1200-250+150)

Cases of disease at time T₂ = 220 among exposed workers

Non-exposed workers                         750        150 start exposure      850 (750-150+250)

Cases of disease at time T₂ = 85 among non-exposed workers

The true risk of disease at time T₂ is 20% among exposed workers (220/1100),
and 10% in non-exposed workers (85/850) (risk ratio = 2.0).

Estimated risk at T₂ of disease among those classified as exposed at T₀: 20%
(i.e., true risk in those exposed) ´ 950 (i.e., 1200-250)+ 10%
(i.e., true risk in non-exposed) ´ 250 = (190+25)/1200 = 17.9%

Estimated risk at T₂ of disease among those classified as non-exposed at
T₀: 20% (i.e., true risk in those exposed) ´ 150 +10%
(i.e., true risk innon-exposed) ´ 600 (i.e., 750-150) = (30+60)/750 = 12%

Estimated risk ratio = 17.9% / 12% = 1.49

Misclassification depends, in this example, on the study design and the characteristics of the population, rather than on technical limitations of the exposure measurement. The effect of misclassification is such that the “true” ratio of 2.0 between the cumulative risk among exposed people and non-exposed people becomes an “observed” ratio of 1.49 (table 1). This underestimation of the risk ratio arises from a “blurring” of the relationship between exposure and disease, which occurs when the misclassification of exposure, as in this case, is evenly distributed according to the disease or health status (i.e., the exposure measurement is not influenced by whether or not the person suffered from the disease that we are studying).

By contrast, either underestimation or overestimation of the association of interest may occur when exposure misclassification is not evenly distributed across the outcome of interest. In the example, we may have bias, and not only a blurring of the aetiologic relationship, if classification of exposure depends on the disease or health status among the workers. This could arise, for example, if we decide to collect biological samples from a group of exposed workers and from a group of unexposed workers, in order to identify early changes related to exposure at work. Samples from the exposed workers might then be analysed in a more accurate way than samples from those unexposed; scientific curiosity might lead the researcher to measure additional biomarkers among the exposed people (including, e.g., DNA adducts in lymphocytes or urinary markers of oxidative damage to DNA), on the assumption that these people are scientifically “more interesting”. This is a rather common attitude which, however, could lead to serious bias.

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