Exposure suspicion bias occurs when the knowledge of the subject’s disease status influences the search for the exposure to the cause.
For instance, when subjects who have the disease undergo a more rigorous search for the cause than those who do not have the disease, leading to an overestimation of the relationship between the risk factor and the disease.
Depending on the situation, exposure suspicion bias can also lead to underestimating the relationship between cause and disease.
This bias affects studies where the disease status is known before the exposure status. Specifically, case-control studies.
A brief overview of the case-control study design:
A case-control study divides participants into 2 groups: those who have the outcome/disease and those who do not have it.
Then the investigators search back in time to see which of these participants were exposed to the risk factor. Finally, they compare the proportion of exposure between groups.
The goal is to determine whether or not the exposure is related to the disease.
Note that the occurrence of exposure suspicion bias is also predicated on knowing the cause of the disease:
If we have no information about which exposure is related to the disease, then by default, we cannot be biased towards a certain exposure more than any other.
Therefore for the bias to occur, the risk factor or cause of the disease must be known to either:
- The researchers responsible for assessing or collecting information about the participants’ exposure status who can be biased in their data collection (The term interviewer bias is used in this case)
- The participants reporting information about themselves whose memory of the exposure may be affected by knowing the cause of their disease (The term recall bias is used in this case)
Exposure suspicion bias example
A systematic review examined exposure suspicion bias among other biases as a possible explanation of the relationship between urinary tract infection (UTI) and delirium in the elderly.
Here’s a summary of its findings:
In this systematic review, the authors argued that some medical professionals believe, based on previous literature, that a urinary tract infection may cause delirium symptoms in the elderly.
This belief leads physicians to search for UTI whenever an elderly patient presents with delirium.
And since asymptomatic bacteriuria is prevalent among this age group, one is likely to get a positive result, therefore, overestimating the relationship between UTI and delirium.
The authors suspect that exposure suspicion bias can explain a lot of the relationship found between UTI and delirium in the reviewed studies.
Whether this bias can explain all the relationship is still unknown.
The only way to determine if delirium is a symptom of a urinary tract infection in the elderly is to conduct a study in which we search for a UTI with equal rigor in both elderly patients who have delirium and those who don’t. Therefore avoiding exposure suspicion bias.
This systematic review concluded that ALL the reviewed studies suffered from methodological flaws and potential bias. Therefore, a rigorous examination of urine elderly patients as a search for a cause of delirium is scientifically unjustifiable.
How to avoid exposure suspicion bias
As discussed above, exposure suspicion bias has 2 sources:
- Study personnel
Here’s how to avoid bias from these 2 sources:
1. Avoid bias from study personnel
Measures must be taken to ensure that there is no disparity when questioning/examining those who have the outcome and those who don’t.
This can be done by training interviewers to be impartial and unbiased in their data collection.
2. Avoid bias from participants
If you have enough data, compare the results of participants who are familiar with the causal relationship between exposure and disease and those who are not. This can provide an estimate for the magnitude of exposure suspicion bias in the study.
Otherwise, use an objective measure of exposition such as:
- Using medical imaging or tests: Such as urine tests to detect an exposure to drugs
- Using bio-markers: Such as hair to detect an exposure to mercury [Source]
- F. B. Hu, Bias from Exposure Suspicion in Case-Control Studies. In Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, and J. L. Teugels, Eds. Chichester, UK: John Wiley & Sons, Ltd, 2014. doi:10.1002/9781118445112.stat05287