Selected Practice Gap

Epidemiology & Population Health

DUE APRIL 1^st

SOURCES OF ERROR IN POPULATION-BASED RESEARCH

INTRODUCTION

This week, you will review different sources of error in population-based research, focusing on bias and confounding. Bias refers to deviations of results, or inferences, from the truth (Friis & Sellers, 2021). There are two overarching types of bias: information bias and selection bias. Both types can be detrimental to the validity and reliability of results. Several strategies exist to help prevent bias, but it is virtually impossible to eliminate bias altogether.

In addition to bias, confounding variables can pose challenges for epidemiologists. Confounding is the masking of an association between an exposure and an outcome because of the influence of a third variable that was not considered in the study design or analysis. For example, if weight loss is the topic of study and exercise is the only variable considered, diet could mask the results of the study.

LEARNING OBJECTIVES

Students will:

• Analyze nursing practice implications of bias, confounding, and random error in epidemiologic and population health research
• Propose strategies to minimize sources of error in population research
• Differentiate epidemiologic measures and measurement errors

This is a graded discussion: 100 points possible

Week 6: Blog

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.

WEEKLY RESOURCES

LEARNING RESOURCES

Required Readings

• 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”

• 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. https://doi.org/10.1186/s12874-019-0779-8
• 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. https://doi.org/10.1186/s13011-021-00398-3
• 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, 1–9Download Preexisting conditions in older adults with mild traumatic brain injuries. Brain Injury, 1–9. Advance online publication. https://doi.org/10.1080/02699052.2021.1976419

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.

Selected Practice Gap:

The selected practice gap pertains to the management of hypertension in elderly patients living in rural areas. In rural communities, access to healthcare services may be limited, resulting in disparities in hypertension management compared to urban areas. The gap lies in the need for tailored interventions to address hypertension control and prevention in this specific population.

Impact of Bias and Confounding:

Awareness of bias and confounding in epidemiologic literature is crucial for understanding the true relationship between interventions and health outcomes in elderly rural populations with hypertension. Selection bias may occur if the study sample predominantly includes individuals who have access to healthcare services or are more health-conscious, leading to overestimation of the effectiveness of interventions. Information bias may arise if self-reported measures of blood pressure control or medication adherence are inaccurate due to memory lapses or social desirability bias, thereby distorting the true effect of interventions. Confounding may occur if factors such as socioeconomic status, education level, or comorbidities are not adequately accounted for in the analysis, leading to erroneous conclusions about the efficacy of interventions.

Strategies to Minimize Bias:

1. Randomized Controlled Trials (RCTs): Implementing RCTs can help minimize selection bias by randomly assigning participants to intervention and control groups, ensuring that baseline characteristics are balanced between the groups. Additionally, blinding participants and researchers to the intervention allocation can reduce information bias by preventing knowledge of treatment assignment from influencing reporting or measurement of outcomes.
2. Adjustment for Confounders: Conducting multivariable regression analyses and propensity score matching can help control for potential confounding variables such as age, gender, socioeconomic status, and comorbidities. By statistically adjusting for these factors, researchers can isolate the true effect of interventions on hypertension outcomes in elderly rural populations.

Effects of Unmitigated Bias on Interpretation:

Failure to minimize bias in epidemiologic studies can lead to erroneous interpretations of study results and subsequent implementation of ineffective interventions in clinical practice. If selection bias is not addressed, interventions may appear more effective than they actually are, leading to unwarranted resource allocation and missed opportunities for improving hypertension management in rural communities. Similarly, failure to account for confounding variables may result in inaccurate estimates of intervention effects, leading to inappropriate clinical decision-making and suboptimal health outcomes for elderly rural patients with hypertension. Therefore, it is essential to employ rigorous study designs and analytical methods to minimize bias and ensure the validity of epidemiologic research findings for informing clinical practice and addressing practice gaps.