Blog: Critiquing Sources Paper

Blog: Critiquing Sources Paper

Chronic obstructive pulmonary disease is a chronic inflammatory disease of the lungs characterized by chronic inflammation and constriction of the lungs and airways. The global burden of this disease has increased, pushing researchers to conduct extensive studies. It is commonly referred to as a smoker’s disease since smoking is the leading cause of COPD. Over the years, research has been conducted to understand the relationship between various factors and disease. Research bias plays a role in the study of COPD patient population affecting their treatment, and researchers can devise strategies to minimize this bias as it significantly affects the interpretation of the study results.

The practice gap

COPD is commonly diagnosed in patients older than 60; however, younger populations may also be affected. Patients present with chronic cough, sputum production, breathlessness, and wheezing. Drugs are given to manage the symptoms and the underlying cause of the disease. This disease commonly occurs in men, but women are also prone to predominantly female smokers. Apart from smoking, other risk factors include occupation exposure, history of respiratory illness during childhood, indoor air pollution, and low socioeconomic status.

Studies have shown that one in four patients with COPD has never smoked; thus, the role of these other factors in disease occurrence should not be downplayed. The role of these factors has not been sufficiently explored. For instance, low socioeconomic status increases the risk of developing COPD due to using firewood for heating, and treatment outcomes may also be worsened due to poor access to quality healthcare. COPD also affects the progression of other diseases, such as; COVID-19, cardiovascular diseases, and gastrointestinal disease, since they indirectly affect the functioning of these systems( Alqahtani , n.d.). COPD mortality and severity of the disease are higher in female smokers than in males; however, the prevalence of this disease is still higher in males.

How COPD treatment is affected by awareness of bias and epidemiological studies

Some sources of error in COPD population research include selection bias. Since most patients suffering from the disease are older than 50, the younger population may need to be more represented. The number of females may also be an inaccurate representation of the entire population. Information bias may also occur in the case of smokers who may not accurately describe their pack years; some may shy away from revealing their low socioeconomic status. Confounding bias is another source of error that may occur when the association between COPD and other risk factors is not sufficiently explored.

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Awareness of these biases is helpful in improving the quality of patient care. More attention can be given to the poorly represented groups. Women and younger adults should be encouraged to be more candid during history taking to better determine the risk for developing COPD and make a better diagnosis, thus improving the quality of treatment. Proper screening of the younger population and among females is also enhanced, and awareness creation is conducted extensively for all age groups and gender (Hopkinson,2023). Understanding that data provided during studies could be an over-or-under estimate, thus care providers go the extra mile. Affordable and pocket-friendly solutions for patients of a low socioeconomic status could be provided, and healthcare could be made more accessible.

Strategies by researchers to minimize bias

Researchers can devise various strategies to minimize bias during studies. One way is choosing the most appropriate study design for COPD patient populations. Some study designs have inherently minimal bias compared to others. Randomization helps minimize selection bias. Experimental studies are based on randomized studies and thus could be employed. Cohort studies help minimize information bias. Double blinding is key in minimizing this bias as neither the study subjects nor the researcher has control over any variables, and the outcome is solely dependent on the treatment. Cohort studies also minimize bias as they focus on the same patient population.

Research analysis plays a vital role in the research process. Various considerations can be made to improve the statistical outcomes. Inferential statistical analysis is helpful for hypothesis testing and determining whether the information obtained is generalizable for the whole population. It helps to give the relationship between two groups and give a cohesive conclusion.

Effects of Biases on Interpretation of study results

Biases result in an over-or-under estimate of the actual burden of disease and other variables and influence the external and internal validity of the study. Selection biases result in an inaccurate estimation of the relationship between different variables, affecting the study’s internal validity. For instance, selecting a patient population majorly made up of males downplays the occurrence of COPD among females. The study results will also represent only some of the population since females will be underrepresented in this case. Conclusions drawn from inaccurate results will also be far from the truth, making the study findings inconsistent and not useful clinically.

COPD is a chronic inflammatory disease of the airways characterized by chronic cough and breathlessness and is commonly diagnosed in older patients. Research studies conducted to reduce disease morbidity and mortality have shown various sources of error, such as selection bias in determining the population sample, information bias, and confounding bias. Awareness of these errors is helpful in caregivers to improve patient care. Researchers can use different study designs, such as cohort studies, to minimize bias. They can also employ varying data analysis methods such as inferential statistical analysis. Healthcare providers should be keen to notice and address disparities existing in the diagnosis and treatment of disease.

Alqahtani, J. S., Oyelade, T., Aldhahir, A. M., Alghamdi, S. M., Almehmadi, M., Alqahtani, A. S., Quaderi, S., Mandal, S., & Hurst, J. R. (n.d.). Prevalence, severity and mortality associated with COPD and smoking in patients with covid-19: A rapid systematic review and meta-analysis. PLOS ONE. Retrieved April 8, 2023, from https://doi.org/10.1371/journal.pone.0233147

COPD 2020: Changes and challenges – american journal of physiology-lung … (n.d.). Retrieved April 8, 2023, from https://journals.physiology.org/doi/full/10.1152/ajplung.00429.2020

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Hopkinson, N. S., Molyneux, A., Pink, J., & Harrisingh, M. C. (2019, July 29). Chronic obstructive pulmonary disease: Diagnosis and management: Summary of updated nice guidance. The BMJ. Retrieved April 8, 2023, from https://www.bmj.com/content/366/bmj.l4486

WEEK 6: BLOG INSTRUCTIONS
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.
Learning Resources:
• Curley, A. L. C. (Ed.). (2020). Population-based nursing: Concepts and competencies for advanced practice (3rd ed.). Springer.
o 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.
o 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, 1–9 Download 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
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.
Please use headings for all paragraphs:
Thanks for all of your help