Vascular Risk Factors, Framingham Risk Score, and COVID-19
Vascular Risk Factors, Framingham Risk Score, and COVID-19 (Batty & Hamer, 2020)
The study by Batty and Hamer (2020) used a prospective cohort design to explore the relationship between cardiovascular disease (CVD) risk factors and COVID-19 hospitalization. The study utilized data from the UK Biobank, which included 356,914 individuals aged 40-69, with baseline health information collected between 2006 and 2010. The authors analyzed established CVD risk factors (obesity, smoking, diabetes, and physical inactivity) along with the Framingham Risk Score to predict COVID-19 severity, using relative risk (RR) as the primary measure of association (Batty & Hamer, 2020).
Strength: One strength of this study is its large sample size, which enhances statistical power and reliability, allowing for more precise estimates of associations between risk factors and COVID-19 outcomes (Friis & Sellers, 2021).
Limitation: A limitation is the long follow-up period, with baseline data collected between 2006 and 2010, raising concerns about the accuracy of risk factor assessments over time and the potential for changes in participants’ health status by the time of the COVID-19 pandemic (Batty & Hamer, 2020).
Population, Data Sources, and Epidemiologic Measures: The population consists of middle-aged adults from the UK Biobank, a cohort of individuals who are self-selected to participate (Batty & Hamer, 2020). Data sources include health information from the UK Biobank and COVID-19 hospitalization data from Public Health England. The study used relative risk (RR) to quantify the association between CVD risk factors and COVID-19 severity (Batty & Hamer, 2020).
Insights and Conclusion: The prospective cohort design was appropriate for examining the relationship between pre-existing risk factors and future disease outcomes, minimizing recall bias. However, the large time gap between baseline data collection and the onset of the pandemic weakens the findings, and the study’s generalizability is limited because the UK Biobank cohort is self-selected. We agree with the researchers’ conclusion that CVD risk factors, particularly the Framingham Risk Score, are associated with COVID-19 hospitalization. However, Further studies with more recent data and broader population samples are necessary to strengthen the findings (Batty & Hamer, 2020).
Exploring E-Cigarette Use and Risky Behaviors in Young Adults: Implications for Cancer Prevention (Hillyer et al., 2021)
Hillyer et al. (2021) conducted a cross-sectional study examining the relationship between e-cigarette use and risky lifestyle behaviors among young adults in primary care and oncology settings. The study included 804 participants from a large urban medical center. The authors analyzed demographic data, smoking habits, e-cigarette use, alcohol consumption, and exposure to second-hand smoke. The researchers used logistic regression to identify associations between e-cigarette use and other risky behaviors, reporting relative risk ratios (RRR).
Strength: A key strength of this study is its large sample size and diverse population, which improves the generalizability of the findings to urban healthcare settings (Hillyer et al., 2021).
Limitation: A limitation of this study is its cross-sectional design, which only allows for the identification of associations rather than causal relationships. It limits the ability to make inferences about the long-term health effects of e-cigarette use on cancer risk (Friis & Sellers, 2021).
Population, Data Sources, and Epidemiologic Measures: The study population comprises young adults in primary care and oncology settings. Data sources include self-reported surveys, which may be subject to recall bias. The epidemiologic measure of association used is relative risk ratios (RRR), which quantify the strength of associations between e-cigarette use and risky behaviors (Hillyer et al., 2021).
Insights and Conclusion: The cross-sectional design was appropriate for identifying patterns of e-cigarette use and risky behaviors in a diverse patient population. However, the lack of longitudinal follow-up limits the ability to assess the long-term health consequences of e-cigarette use. We agree with the researchers’ conclusion that e-cigarette use in young adults should be addressed as part of cancer prevention efforts, especially given the co-occurrence of other risky behaviors like smoking and alcohol use (Hillyer et al., 2021). However, further longitudinal studies would provide more robust insights into the long-term cancer risk associated with e-cigarette use.
Conclusion
Both studies provide valuable insights into their respective health issues. The prospective cohort study by Batty and Hamer (2020) demonstrates a strong link between cardiovascular risk factors and COVID-19 hospitalization, while the cross-sectional study by Hillyer et al. (2021) explores the relationship between e-cigarette use and risky behaviors among young adults. Although the study designs were appropriate for their objectives, both studies face limitations in generalizability and the ability to establish causal relationships. Further research with updated, longitudinal data is needed to strengthen these findings and inform evidence-based interventions.
