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.
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.
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.
Week 6 Blog
My chosen health issue is Alzheimer’s disease in older adults. The practice gap relevant to this health issue and population is underutilization of early detection and diagnosis in mental health settings. While early detection and diagnosis allow timely management and intervention, many cases of Alzheimer’s disease in older adults go undetected and unnoticed (Li et al., 2023). As a result, there is delayed treatment and intervention, increased healthcare costs, increased burden on caregivers, and increased cases of psychological and social dysfunction among the affected people (King et al., 2020).
When working with older people with Alzheimer’s disease, it is crucial to be aware of bias and confounding variables. In the chosen practice gap, selection bias may occur if the sample under examination fails to accurately represent older people with Alzheimer’s condition (Curley, 2020). As a result, this may distort study conclusions about the appropriateness of diagnostic tools and the effectiveness of the treatment interventions. Besides, information bias may occur if the methods used to collect data for assessing the patients’ mental and cognitive function are not standardized across studies, leading to variability of results. Confounding variables, including socioeconomic status and comorbidities, may impact the results (Friis & Sellers, 2020). This makes it complex to distinguish the actual effects of early detection and diagnosis of Alzheimer’s disease treatment and management in older people.
To minimize study bias, researchers can employ various strategies. The first strategy is to carefully create a study design that ensures the sample accurately represents the target population (Curley, 2020). This can be realized by using comprehensive sampling techniques that ensure that the population fully represents older people at risk of Alzheimer’s disease. For example, using random sampling technique can help reduce selection bias. This can be achieved by embracing a community-based participatory approach, where community members are actively involved in the research process. The second strategy can be using statistical techniques to improve control for confounding variables affecting study outcomes.
Failure to minimize bias and confounding in studies may distort the interpretation of study results. For example, failure to minimize selection bias may lead a researcher to overestimate the benefits of a screening tool (Curley, 2020). Also, if information bias is not minimized, research findings across studies would be inconsistent, making it complex to make accurate and valid conclusions (Friis & Sellers, 2020). In conclusion, the failure to minimize bias would result in misleading and incorrect conclusions from the study results. Further, using incorrect conclusions can have adverse effects, including implementation of harmful and ineffective interventions.
References
- Curley, A. L. C. (Ed.). (2020). Population-based nursing: Concepts and competencies for advanced practice (3rd Ed.). Springer. Chapter 4, “Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part II”
- Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th Ed.). Jones & Bartlett. Chapter 10, “Data Interpretation Issues”
- King, M., Peckham, A., Marani, H., Roerig, M., Yung, S., McGrail, K., & Marchildon, G. (2023). Gaps in the system: supporting people living with dementia. Journal of Aging & Social Policy, 1-21. https://doi.org/10.1080/08959420.2023.2226341
- Li, Q., Yang, X., Xu, J., Guo, Y., He, X., Hu, H., & Bian, J. (2023). Early prediction of Alzheimer’s disease and related dementias using real‐world electronic health records. Alzheimer’s & Dementia, 19(8), 3506-3518. https://doi.org/10.1002/alz.12967