Ascertainment bias

***Systematic differences in the identification of individuals included in a study or distortion in the collection of data in a study.***

```
 **Index of biases** **| Editing advice |**

```

## Background

Ascertainment bias arises when data for a study or analysis is collected (or surveyed, screened, or recorded) such that some members of the intended population are less likely to be included than others. The resulting study sample becomes biased, as it is systematically different from the intended population. Ascertainment bias is related to sampling and selection bias.

It can happen when there is more intense surveillance or screening for the outcome among exposed individuals than among unexposed individuals, or differential recording of the outcome.

Ascertainment bias can occur in screening, where take-up can be influenced by factors such as cultural differences. It can occur in case-control studies in the initial identification of cases and controls, which can be skewed by relevant exposures,  leading to biased relationships.

In a clinical trial, if allocation concealment and blinding are lacking, then outcome ascertainment can be overtly influenced by knowledge of the allocation.

## Example

In a Cochrane systematic review “Helmets for preventing head and facial injuries in bicyclists”, the authors examined information from five case-control studies. These studies obtained information from cyclists who had crashed or fallen. Cases were cyclists suffering head, or facial injuries and controls were cyclists suffering other injuries. In this situation, ascertainment bias could occur if information on helmet wearing was obtained differently for cases and controls.

In describing how to avoid ascertainment bias, the authors write:“In order to study facial injuries, cases should be limited to serious injuries (lacerations and fractures) that would result in an emergency department visit whether or not a head injury was also present” People with minor facial injuries may be identified because bicyclists seek care for head injuries.” This example illustrates the notion of biased screening for the outcome leading to ascertainment bias.

A database analysis found that rates of Alzheimer’s disease were lower among higher users of statins; a letter commenting on this study suggested that some or all of this observed association may be due to ascertainment bias: Alzheimer’s diagnosis requires contact with a doctor and statins use might be associated with frequency of visiting the doctor.

## Impact

We did not identify studies reporting the impact of ascertainment bias.

## Preventive steps

Appropriate inclusion of cases and controls within case-control studies is essential to avoid ascertainment bias. In cohort studies, knowledge of exposures of interest should be kept separate from screening, identification and recording of relevant outcomes of interest. In clinical trials, allocation concealment and blinding of all study staff are critical to maintaining a low risk of bias throughout the trial.

## Cite as

Catalogue of Biases Collaboration, Spencer EA, Brassey J, **Ascertainment bias**. In: Catalogue Of Bias 2017: https://catalogofbias.org/biases/ascertainment-bias/

## Sources

Berkman ND, et al.  Agency for Healthcare Research and Quality (US). The empirical evidence of bias in trials measuring treatment differences. 2014 Sep. Report No.: 14-EHC050-EF. AHRQ Methods for Effective Health Care

Freedman DM, Pfeiffer RM. Ascertainment Bias in Statin Use and Alzheimer Disease Incidence. JAMA Neurol. 2017 Jul 1;74(7):868

Thompson DC et al. Helmets for preventing head and facial injuries in bicyclists. Cochrane Database of Systematic Reviews 1999, Issue 4. Art. No.: CD001855. DOI: 10.1002/14651858.CD001855

Zissimopoulos JM et al. Sex and Race Differences in the Association Between Statin Use and the Incidence of Alzheimer Disease. JAMA Neurol. 2017 Feb 1;74(2):225-232