Blog: Critiquing Sources of Error in Population Research to Address Gaps in Nursing Practice
As a DNP-educated nurse, part of your role will be to identify the differences, or gaps, between current knowledge and practice and opportunities for improvement leading to an ideal state of practice. Being able to recognize and evaluate sources of error in population research is an important skill that can lead to better implementation of evidence-based practice.
In order to effectively critique and apply population research to practice, you should be familiar with the following types of error:
Selection Bias
Selection bias in epidemiological studies occurs when study participants do not accurately represent the population for whom results will be generalized, and this results in a measure of association that is distorted (i.e., not close to the truth). For example, if persons responding to a survey tend to be different (e.g., younger) than those who do not respond, then the study sample is not representative of the general population, and study results may be misleading if generalized.
Information Bias
Information bias results from errors made in the collection of information obtained in a study. For example, participants’ self-report of their diet may be inaccurate for many reasons. They may not remember what they ate, or they may want to portray themselves as making healthier choices than they typically make. Regardless of the reason, the information collected is not accurate and therefore introduces bias into the analysis.
Confounding
Confounding occurs when a third variable is really responsible for the association you think you see between two other variables. For example, suppose researchers detect a relationship between consumption of alcohol and occurrence of lung cancer. The results of the study seem to indicate that consuming alcohol leads to a higher risk of developing lung cancer. However, when researchers take into account that people who drink alcohol are much more likely to smoke than those who do not, it becomes clear that the real association is between smoking and lung cancer and the reason that those who consume alcohol had a higher risk of lung cancer was because they were also more likely to be smokers. In this example, smoking was a confounder of the alcohol-lung cancer relationship.
Random Error
The previous three types of errors all fall under the category of
systematic errors, which are reproducible errors having to do with flaws in study design, sampling, data collection, analysis, or interpretation.
Random errors, on the other hand, are fluctuations in results that arise from naturally occurring differences in variables or samples. While unavoidable to a small degree even under the most careful research parameters, these types of errors can still affect the validity of studies.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
· Curley, A. L. C. (Ed.). (2024).
Population-based nursing: Concepts and competencies for advanced practice (4th ed.). Springer.
· Chapter 5, “Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part II” (pp. 106-128)
Friis, R. H., & Sellers, T. A. (2021).
Epidemiology for public health practice (6th ed.). Jones & Bartlett.
· Chapter 10, “Data Interpretation Issues”
· Enzenbach, C., Wicklein, B., Wirkner, K., & Loeffler, M. (2019).
Evaluating selection bias in a population-based cohort study with low baseline participation: The LIFE-Adult-StudyLinks to an external site..
BMC Medical Research Methodology,
19(1), Article 135.
· Khalili, P., Nadimi, A. E., Baradaran, H. R., Janani, L., Rahimi-Movaghar, A., Rajabi, Z., Rahmani, A., Hojati, Z., Khalagi, K., & Motevalian, S. A. (2021).
Validity of self-reported substance use: Research setting versus primary health care settingLinks to an external site..
Substance abuse Treatment, Prevention, and Policy,
16(1), Article 66.
· Karr, J. E., Iverson, G. L., Isokuortti, H., Kataja, A., Brander, A., Öhman, J., & Luoto, T. M. (2021).
Preexisting conditions in older adults with mild traumatic brain injuries.
Brain Injury, 1–9
Download Preexisting conditions in older adults with mild traumatic brain injuries. Brain Injury, 1–9. Advance online publication.
To Prepare:
· Review this week’s Learning Resources, focusing on how to recognize and distinguish selection bias, information bias, confounding, and random error in research studies.
· Select a health issue and population relevant to your professional practice and a practice gap that may exist related to this issue.
· Consider how each type of measurement error may influence data interpretation in epidemiologic literature and how you might apply the literature to address the identified practice gap.
· Consider strategies you might use to recognize these errors and the implications they may have for addressing gaps in practice relevant to your selected issue.
By Day 3 of Week 6
Post a cohesive scholarly response that addresses the following:
· Describe your selected practice gap.
· Explain how your treatment of this population/issue could be affected by having awareness of bias and confounding in epidemiologic literature.
· Explain two strategies researchers can use to minimize these types of bias in studies, either through study design or analysis considerations.
· Finally, explain the effects these biases could have on the interpretation of study results if not minimized.
By Day 6 of Week 6
Respond to
at least two colleagues
on two different days in one or more of the following ways:
· Ask a probing question, substantiated with additional background information, evidence, or research.
· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
· Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
· Validate an idea with your own experience and additional research.
· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
Vivian
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Practice Gap Selected
· The gap exists due to inadequate and infrequent treatment of hypertension in patients 65 years and older in primary care settings. Many times, healthcare professionals fail to follow the proper therapeutic processes for this age group, and it can lead to poor results for the patient.
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Influence of Bias and Confounding on Treatment
· With respect to this practice guideline gap, it matters how bias and confounding affect treatment and its results. For instance, there is selection bias when strategies are based on patients who show up foor appointments and are health conscious, as this does not depict the whole population of older adults suffering from hypertension. Some older patients may unintentionally exaggerate medication compliance for various reasons, making compliance assessment more difficult (Khalili et al., 2021). Other chronic illnesses prevalent in older populations, like diabetes or obesity, may result in blurring of the use and effectiveness of the treatment. Thus, this may lead to under-treatment or over-treatment (Friis & Sellers, 2021).
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Strategies to Minimize Bias
· Minimizing selection bias is best achieved by employing random control trials (RCTs). RCTs are beneficial because they issue randomization, which theoretically gives every participant an equal chance of being given the intervention. This significantly lowers the impact of confounding factors and strengthens the validity of causative conclusions regarding the treatment of hypertension in the elderly (Friis & Sellers, 2021).
· Similarly, researchers may use multivariate techniques, for example, logistic regression or propensity score matching, to account for confounding factors statistically(Curley, 2024). The greater the number of confounding factors, such as age, sex, lifestyle, and other medical issues, that are removed, the greater the real impact of the hypertension interventions.
· Implications of Bias if Not Minimized
· The literature’s biases and other external factors affecting treatment need to be addressed before coming up with a management plan for hypertension for older adults. For example, there could be selection bias in which patients who visit the clinic are overrepresented and this is a real problematic scenario because these patients have been given a better chance of recovery than they actually have. Older patients might ignore reporting their medication adherence due to recall and social desirability, undermining the effectiveness of the intervention (Khalili et al., 2021). If present, Other conditions, such as diabetes and obesity, affect the validity of the expected outcomes and can result from the hypertension management strategies and cause adverse effects on the treatment (Friis & Sellers, 2021).
· References
· Curley, A. L. C. (Ed.). (2024). Population-based nursing: Concepts and competencies for advanced practice (4th ed.). Springer.
· Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
· Khalili, P., Nadimi, A. E., Baradaran, H. R., Janani, L., Rahimi-Movaghar, A., Rajabi, Z., Rahmani, A., Hojati, Z., Khalagi, K., & Motevalian, S. A. (2021). Validity of self-reported substance use: Research setting versus primary health care setting. Substance Abuse Treatment, Prevention, and Policy, 16(1), Article 66.
Links to an external site.
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