Bias with Epidemiology & Selection and Information Response

IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS:

Please note that each and every assignment has its own word limit.

 

 

There are two major types of bias with epidemiology, selection and information bias. Selection bias occurs when the subjects studied are not representative of the target population about which conclusions are to be drawn. It can also be defined as a bias resulting from inappropriate selection of controls in case-control studies, bias resulting from differential loss-to-follow up, incidence–prevalence bias, volunteer bias, healthy-worker bias, and nonresponse bias. Epidemiologists apply the term “selection bias” to many biases, including bias resulting from inappropriate selection of controls in case-control studies, bias resulting from differential loss-to-follow up, incidence–prevalence bias, volunteer bias, healthy-worker bias, and nonresponse bias (Robins, 2014).

Information bias results from systematic differences in the way data on exposure or outcome are obtained from the various study groups. This may mean that individuals are assigned to the wrong outcome category, leading to an incorrect estimate of the association between exposure and outcome (Hennekens, 1987).

An example of a bias study by Roerecke, bias in alcohol epidemiology is a serious issue that needs careful investigation due to the possibility for selection bias with alcohol. Investigations of potential bias are important in any scientific analysis to derive meaningful conclusions about the validity of the risk relations between exposures and disease outcomes. The risk of bias in any epidemiological (observational) study is a serious issue and every effort should be undertaken to avoid, reduce and investigate potential risk of bias. To minimize the bias risk with ETOH, one would have to look at the cause-specific disease outcomes and not overall mortality, because we know that alcohol consumption has many differential biological effects on the human body (Roerecke, 2017).
The implications of making inferences based on data with bias includes lack of proper treatment and missed opportunities to address the illness

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References :

Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987.

Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. 2014;11:313–320.

Roerecke, M. (2017). On bias in alcohol epidemiology and the search for the perfect study. Addiction, 112(2), 217–218. Retrieved from https://search-ebscohost-com.lopes.idm.oclc.org/login.aspx?direct=true&db=s3h&AN=120660150&site=eds-live&scope=site

 

Respond to the bold paragraph ABOVE by using one of the option below… in APA format with At least two references and a minimum of 200 words….. .(The List of References should not be older than 2016 and should not be included in the word count.)

 

 

 

• Ask a probing question.
• Share an insight from having read your colleague’s posting.
• Offer and support an opinion.

 

 

 

• Validate an idea with your own experience.
• Make a suggestion.
• Expand on your colleague’s posting.

 

 

 

 

 

 

Be sure to support your postings and responses with specific references to the Learning Resources.

It is important that you cover all the topics identified in the assignment. Covering the topic does not mean mentioning the topic BUT presenting an explanation from the context of ethics and the readings for this class

 

To get maximum points you need to follow the requirements listed for this assignments 1) look at the word/page limits 2) review and follow APA rules 3) create subheadings to identify the key sections you are presenting and 4) Free from typographical and sentence construction errors.

REMEMBER IN APA FORMAT JOURNAL TITLES AND VOLUME NUMBERS ARE ITALICIZED.