OBSERVATIONAL STUDY DESIGNS
DISCUSSION RESPONSE
OBSERVATIONAL STUDY DESIGNS
DISCUSSION RESPONSE
Post at least two substantive responses to peers who analyzed at least one different article in their initial post. Include information from the Learning Resources in your responses as appropriate. You may expand on each peer’s posting with additional insight and resources about study designs, ask a question to further the discussion, or offer polite disagreement or critique supported with evidence. You may also make a suggestion or comment that guides or facilitates the discussion. At least one of your response posts should address the applicability of observational studies for improving population health status.
Observational Study Design
The articles selected for this study include: “Vascular risk factors, Framingham risk score, and COVID-19: community-based cohort study” by Batty & Hamer, (2020) and “E-cigarette use among young adult patients: The opportunity to intervene on risky lifestyle behaviors to reduce cancer risk” by Hilyer et al. (2021).
The article by Batty & Hamer, (2020) employed a prospective cohort design, utilizing data from the UK Biobank to investigate the association between classic cardiovascular disease risk factors and the occurrence of COVID-19 in a community-based setting. A sample size of 502,655 individuals aged 40 to 69 years who were primarily from England was included. The Framingham risk score which integrated various factors such as age, cholesterol levels, blood pressure, smoking, and diabetes status, was employed as a predictive tool. On the other hand, Hilyer et al. (2019) conducted a health needs survey via email among 804 adult patients in 2019. The objective was to investigate the prevalence of e-cigarette use and its associations with smoking status, personal history of cancer, alcohol use, and second-hand tobacco smoke exposure. Statistical analysis revealed higher prevalence of e-cigarette use among young adults aged 18–24 years, individuals engaging in very often binge drinking, and those exposed to tobacco smoke at home. Risk ratios (RR) with corresponding 95% confidence intervals (95% CI) were computed to quantify these associations.
The first study primary strength was in its large sample size and prospective nature. This allowed for the assessment of temporality and reduction in the likelihood of recall bias. However, a limitation is the reliance on self-reported data for certain variables, which may introduce measurement error and potential misclassification bias. The population consisted of 502,655 individuals aged 40–69 years from various UK centers, with data collected between 2006 and 2010. Epidemiologic measures of association, such as risk ratios and confidence intervals, were employed to quantify the relationships between the variables being examined. Overall, the study design appears appropriate for examining the research question, as it leverages existing data from a large cohort with extensive baseline information. However, further validation studies and exploration of potential confounders may enhance the robustness of the findings.
In the second article, one strength of this study’s design is its focus on a specific population subset. This allowed for targeted investigation of e-cigarette usage patterns. However, its main limitation is the reliance on self-reported data, which may be subject to recall and social desirability biases. The epidemiologic measures of association utilized in this study include risk ratios and corresponding confidence intervals. While the design may be appropriate for exploring e-cigarette use patterns within this specific patient population, caution should be exercised in generalizing findings beyond this context.
Based on my assessment of the researchers’ conclusions, in the case of the first study, while the association between CVD risk factors and COVID-19 infection appears plausible based on biological mechanisms, further exploration of potential confounders is warranted to strengthen the evidence base. Similarly, for the second study, the associations between e-cigarette use and demographic and behavioral factors align with existing literature, however caution needs to be exerted due to potential biases inherent in self-reported data. Therefore, while the researchers’ conclusions offer valuable insights, additional validation efforts may further bolster their validity and generalizability.
References
Batty, G. D., & Hamer, M. (2020). Vascular risk factors, Framingham risk score, and COVID- 19: community-based cohort study.
Cardiovascular research,
116(10), 1664-1665.
Hillyer, G. C., Nazareth, M., Lima, S., Schmitt, K. M., Reyes, A., Fleck, E., … & Terry, M. B. (2022). E-cigarette use among young adult patients: the opportunity to intervene on risky lifestyle behaviors to reduce cancer risk.
Journal of community health,
47(1), 94-100.
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Describe at least one strength and one limitation of each study’s design.
The strength that I found in the first literature that I read is the ability for the hospital to study more than one risk factor at a time although the primary focus is hospital falls (Najafpour et al., 2019). The limiting factor is that patients are able to report self falls in which there can be false or non-accurate information.
The strength of the second literature that focused on the detection of covid in New York, has several strengths, one being that it did not focus on one racial group and incorporated a diverse research in which makes the data more accurate (Whittle et al., 2020). One limitation that this article has is the circumference of that data that was taken.
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Identify the population, data sources, and epidemiologic measures of association that the authors used.
The data sources for the first article were collected from the hospital data which include interviews from patients and families in which resulted in 185 cases of reported falls (Whittle et al., 2019).
The New York article has cases that report from the department of health and is made up from diverse cultures from all backgrounds with no limitation to age and or income within that specific circumference in which could narrow down to a socioeconomic class if deemed (Najafpour et al., 2019).
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Finally, share your insights about the appropriateness of the design for the study. Do you agree with the researchers’ choice of design?
I think that the design of the first study could have provided more accurate information seeing as though there were some self reported falls in the data in which skews the accuracy of the report. Although I do not agree with how the data was received, the choice of design was appropriate.
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Do you agree with the researchers’ conclusions? Justify your reasoning.
I agree with the conclusion of all of the studies that I read. I agree that the information provided was beneficial and it came from decent reliable information. The information gathered in the covid article was gathered in a certain circumference in which could have been seen as bias due to whatever socioeconomic class thats predominantly in that area.
References:
Najafpour, Z., Godarzi, Z., Arab, M., & Yaseri, M. (2019).
Risk factors for falls in hospital in-patients: A prospective nested case control studyLinks to an external site.Links to an external site.
.
International Journal of Health Policy and Management,
(5), 300–306.
to an external site.
Whittle, R., & Diaz‐Artiles, A. (2020, September 4).
An Ecological Study of Socioeconomic Predictors in Detection of COVID-19 Cases Across Neighborhoods in New York City – BMC Medicine. BMC Medicine.
to an external site.
