Data Interpretation in Epidemiology Discussion Paper

Data Interpretation in Epidemiology Discussion Paper

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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. Data Interpretation in Epidemiology Discussion Paper

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:

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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 Data Interpretation in Epidemiology Discussion Paper

The Selected Healthcare Gap

The selected healthcare gap relates to the rising burden of chronic cardiovascular diseases affecting health-related quality of life. The condition is among the leading causes of morbidity and mortality, affecting some individuals and populations unequally, resulting in health disparities. One of the key health disparities affecting cardiovascular diseases is gender-related health disparities, affecting more women who experience disparities in the diagnosis, treatment, and support from the community (Tschiderer et al., 2023). The existing healthcare gap associated with cardiovascular diseases in women is associated with underdiagnosis, inadequate healthcare resources, and inadequate knowledge and understanding of the disease.

Awareness of Bias and Confounding

As the affected population, women must implement evidence-based strategies and resource mobilization to address the specific health-related gaps and disparities. Addressing the issue requires accurate data to determine the effectiveness of these measures in addressing them. Biases can lead to inaccurate data estimates for the treatments and their effects. Therefore, it can result in inadequate or misleading evidence in developing evidence-based practice. In addition, confounding variables can affect the degree of relationships between the dependent and independent variables, resulting in the research team’s inability to draw generalizable conclusions about the effectiveness of various therapies in managing and preventing cardiovascular diseases among women (Mathur & VanderWeele, 2021). Data Interpretation in Epidemiology Discussion Paper

Minimizing Bias and Confounding

The research design selected in exploring the underlying variables and issues is significant since it defines the research methods used. Professionals should select research methods that allow randomization of subjects to prevent biases and ensure that every member of the study population has an equal chance for participation in the research process. Randomization prevents selection bias, affecting the results’ validity and reliability (Polit & Beck, 2022). Therefore, applying a randomized controlled research design is a perfect research approach that will allow the research team to control biases and confounding and ensure that the research results align with the cardiovascular risks, management, and effectiveness evaluation for cardiovascular diseases among women.

Effects of Uncontrolled Biases and Confounding

The validity and reliability of the research are critical elements that inform the team and stakeholders of the effectiveness of the research in exploring the research problem or analyzing the effectiveness of these measures in addressing the underlying gaps. The research team may underestimate or overestimate the treatment effects without controlling these factors. Failure to accurately estimate the treatment effects may affect the ability of the team to determine the most effective approach or technique for preventing chronic cardiovascular diseases among women. In this approach, a false negative outcome may limit the ability of the healthcare team to consider effective treatment ineffective; thus, the treatment approach may be underestimated (Polit & Beck, 2022). Ineffective management of confounding and biases may result in expanding health disparities affecting women since no effective treatment approach will be selected to address the underlying healthcare gap. Therefore, instead of handling the health disparities, the issues contribute to the expanding health disparities, impact the ability of the healthcare team to process the issues, understand health disparities, and implement population-centered approaches for managing this gap. Data Interpretation in Epidemiology Discussion Paper

Conclusion

Addressing bias and confounding in a critical approach for ensuring validity and reliability of the research. Epidemiologists must identify and address these issues before conducting epidemiological studies and ensure the results generated are generalizable. Researchers can apply strategies such as applying randomized controlled research designs to explore the relationships and develop accurate results that can be generalized to the entire study population of interest. Data Interpretation in Epidemiology Discussion Paper

References

Mathur, M. B., & VanderWeele, T. J. (2021). Methods to address confounding and other biases in meta-analyses: Review and recommendations. Annual Review of Public Health, 43(1). https://doi.org/10.1146/annurev-publhealth-051920-114020

Polit, D., & Beck, C. (2022). Essentials of nursing research: Appraising evidence for nursing practice. (10th ed.). Wolters Kluwer Medical.

Tschiderer, L., Seekircher, L., Willeit, P., & Peters, S. (2023). Assessment of cardiovascular risk in women: Progress so far and progress to come. International Journal of Women’s Health, Volume 15, 191–212. Data Interpretation in Epidemiology Discussion Paper