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 evi
· RESPOND TO THIS DISCUSSION POST
Izoduwa
Addressing Bias in Epidemiologic Research: Breast Cancer Screening in Older Women
Breast cancer screening is a critical public health measure, yet a notable practice gap exists in screening rates among older women. Many women over 65 do not undergo regular mammograms, despite the risk of developing breast cancer increasing with age. Factors such as misconceptions about risk, lack of provider recommendations, and barriers to access contribute to this gap. Addressing this issue requires an understanding of biases in epidemiologic research that influence decision-making and policy implementation.
Awareness of Bias and Its Impact on Treatment
Recognizing biases in epidemiologic literature is essential when interpreting research findings and applying them to practice. Several biases can impact how breast cancer screening recommendations are developed and implemented:
·
Selection Bias: If studies on breast cancer screening primarily include younger women or those who actively seek healthcare services, the findings may not accurately represent the needs of older women. This can lead to underestimating the importance of screening in this population.
·
Information Bias: Self-reported data on screening practices may be inaccurate. Women might overreport their participation in screenings due to recall issues or social desirability bias, leading to misleading conclusions about screening rates.
·
Confounding: Other factors, such as comorbidities, can influence the relationship between age and screening rates. If researchers do not adjust for these factors, it may appear that age alone is the determining factor in low screening rates when, in reality, other issues like mobility limitations or provider recommendations play a role.
·
Random Error: Natural variations in study samples can lead to fluctuations in results. While small random errors are inevitable, they can still affect data interpretation.
Strategies to Minimize Bias
To ensure accurate and applicable research findings, researchers can use several strategies to minimize bias:
1.
Randomized Sampling and Inclusion Criteria: Ensuring that study participants represent the broader population of older women can reduce selection bias. Researchers should actively recruit diverse participants from different healthcare settings.
2.
Validated Data Collection Methods: Using electronic health records instead of self-reported data can improve accuracy and reduce information bias. Additionally, adjusting for confounding variables in statistical analyses helps ensure that conclusions are based on the true association between variables.
Consequences of Unaddressed Bias
If biases are not accounted for, study results can be misleading, leading to ineffective or inappropriate recommendations. For example, if selection bias skews findings toward younger women, guidelines may not prioritize breast cancer screening for older women, potentially resulting in late diagnoses and poorer outcomes. Similarly, failing to adjust for confounders could lead to incorrect assumptions about why screening rates are low, leading to ineffective interventions.
Conclusion
Understanding and addressing biases in epidemiologic research is crucial for closing practice gaps, such as low breast cancer screening rates in older women. By implementing strategies to minimize bias, researchers can provide more accurate data, leading to better-informed policies and improved patient care. As healthcare professionals, being aware of these biases allows for critical evaluation of research and better decision-making in clinical practice.
References
· Nelson, H. D., Fu, R., Cantor, A., Pappas, M., Daeges, M., & Humphrey, L. (2016). Effectiveness of breast cancer screening: Systematic review and meta-analysis to update the 2009 U.S. Preventive Services Task Force recommendation.
Annals of Internal Medicine, 164(4), 244-255.
· Smith, R. A., Andrews, K. S., Brooks, D., Fedewa, S. A., Manassaram-Baptiste, D., Saslow, D., & Wender, R. C. (2019). Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening.
CA: A Cancer Journal for Clinicians, 69(3), 184-210.
· Wernli, K. J., DeMartini, W. B., Ichikawa, L., Lehman, C. D., & Buist, D. S. (2017). Patterns of breast magnetic resonance imaging use in community practice.
Journal of the National Cancer Institute, 109(1), djw297.
