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Bias in Epidemiologic Research


Potential biases are likely to be present and affect epidemiologic research at the level of design, implementation, and analysis of a study. Bias is defined as systematic errors that affect the epidemiologic research, hence leading to invalid measures of association and altered study outcomes (Aschengrau & Seage, 2008). There are two main categories of biases in epidemiologic research, and they include selection bias and information bias (Aschengrau & Seage, 2008).

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Epidemiologic biases

In epidemiologic research, biases occur when the estimated association varies or deviates significantly from the true measure of association. The bias has an impact on the Risk Ratio (RR) and Odd Ratio (OR) estimates. Random or systematic errors are thought to be the leading cause of epidemiologic biases.

Selection error is a bias that originates from improper or differential procedures of selection of study participants from the target population to become members of the study population (Dawn, Balkrishnan & Feldman, 2008). This bias usually results in variance between the exposure and disease for persons who complete the study as compared to the target population. Selection bias leads to distortion of measures of association such as odd ratio, risk ratio, or rate ratio (Rothman, Greenland & Lash, 2008).

Information error in epidemiologic research is the distortion of measures of association estimates due to inaccurate measurements or classification of exposure or disease status (Rothman, Greenland & Lash, 2008). This error arises even if the measure estimates produced are equal between diseased or non-diseased, or between diseased and non-diseased study members. Confounding bias is common in the pharmaceutical field and occurs when the in question is selectively used or not used by the individual who developed the outcome of interest. It is common where the casual relationship between the drug and its effects disease treatment cannot sufficiently be established (Fletcher & Fletcher, 2005).

The cohort study looks at the association of pesticides and the occurrence of childhood leukemia is likely to be affected with selection bias. The selection of children exposed to pesticides to determine whether the outcome of interest, that is, occurrence of leukemia in high and low area, may be biased. The systematic error in the design, conduct, and analysis of the study results about the level of exposure to leukemia is likely to be overestimated because there are children with family history of leukemia that are likely to participate (Gabor, et. al, 2008).

The second scenario where the HIV positive and negative people are asked by the number of sexual activities they have had in their lifetime is affected by information bias. The information may be inaccurate depending on the status of the persons, hence may overestimate or underestimate the measure of association between the positive and negative persons (Tripepi, Jager, Dekker & Zoccali, 2010). Selection bias is likely to occur when a cohort study is done to establish the relationship between exposure to PCB and the chances of getting cancer in the duration of 20 years. This is attributed to loss of follow-up participants to assess their disease exposure status (Schoenbach, 1999). This leads to underestimation of measures of association such as RR and OR. Confounding bias is likely to affect the clinical trial in scenario four. The clinical trials are conducted by a doctor who has prior knowledge about the outcome of interest, hence it will effect on the outcome of the outcome of the drug (Fletcher & Fletcher, 2005).


When conducting epidemiologic research it is important to consider all biases that are likely to affect the outcomes of the study. Proper measures should be introduced to minimize the biases, thereby improving on the quality of the outcomes. It begins with proper research design and efficient conducting of the research.

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Aschengrau, A., & Seage, G. R. (2008). Essentials of epidemiology in public health. Sudbury, Mass: Jones and Bartlett Publishers.

Dawn, A. G., Balkrishnan, R., & Feldman, S. R. (2008). Systematic selection bias: A cause of dramatic errors in the inference of treatment effectiveness. Journal of Dermatological Treatment, 19(2), 68-71. Web.

Fletcher, R. H., & Fletcher, S. W. (2005). Clinical epidemiology: The essentials. Baltimore, Md: Lippincott Williams & Wilkins.

Gabor, M., John J., S., Paul, W., Marilyn, B., & Mary L., M. (2008). Assessment of Selection Bias in the Canadian Case-Control Study of Residential Magnetic Field Exposure and Childhood Leukemia. American Journal of Epidemiology, 167(12), pp. 1504-1510.

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology. Philadelphia: Lippincott Williams & Wilkins.

Schoenbach, J. V. (1999). Analytic study designs. Web.

Tripepi, G., Jager, K. J., Dekker, F. W., & Zoccali, C. (2010). Selection Bias and Information Bias in Clinical Research. Nephron Clinical Practice, 115(2), c94-c99. Web.

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