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I need someone to paraphrase this discussion post by tomorrow morning!!! no plagiarism or AI use please! Observational Study Designs Strengths and limitations of the Studies' research designs Batty and Hamer (2020) employed a prospective cohort study design whereby participants with initial similar conditions wereobserved over a period of time to determine how exposure led tocertain outcomes. One of this design's key strengths is its precise temporal sequence, which enables researchers to establish a clear pathway between exposure and outcomes, an effective approach to deducing causality. This design also has minimal recall bias because the data collection processes are prospective, and the approach barely relies on subject recalls, consequently reducing errors resulting from recall bias. However, the study design is time-consuming, a factor that serves as a limitation. As illustrated by the Framingham Heart Study, cohort studies require long follow-up periods, thus delaying findings and results. Delayed results and findings may limit the applicability and generalizability because a study fails to serve its intendedpurpose. On the other hand, Hillyer et al. (2021) adopted a cross-sectional study design, which entails collecting data from a population or representative group of the population at a specific time, as explained by Bell et al. (2022). The notable strength of this design is that researchers are able to obtain findings relatively faster and cheaper, which is also explained in the Let's Learn Public Health (2017) video. The Framingham Heart Study also demonstrates that compared to other study designs, the cross-sectional design is relatively cheap to deploy since it is quick and requires minimal resources. Another strength is that it assesses the prevalence of a disease, therefore offering a pathway for understanding health conditions and behaviors within a population. However, one major limitation of a cross-sectional study design is that it lacks temporal information, creating complications in determining the causation and relationship of health conditions. As discussed in Let's Learn Public Health (2017), the cross-sectional approach only records data from a single-entry point, making it difficult to establish the direction and relationship between variables. Population, Data Sources, and Epidemiologic Measures of Association Batty and Hamer (2020) recruited participants from the UK Biobank, targeting adults aged between 40 and 69. As such, the UK BioBank served as a primary data source, and it was used to acquire baseline information such as age, ethnicity, blood pressure, and diabetes diagnosis. Public Health England (PHE) also served as a data source for the study since it provided the authors with Covid-19 test results. The authors used Framingham Risk Score (RFS) as a key epidemiological measure of association, whereby COVID-19 risks were categorized into groups and computed using categories such as age, sex, and diabetes status.  Hillyer et al. (2021) recruited adults aged 18 years and above from primary care and oncology clinics based in New York. Additionally, the authors drew data from electronic health records and self-administered email surveys. The epidemiological measures of association of the study included Risk ratios (RRs) with 95% confidence intervals (CIs), whereby e-cigarette usage was compared across various demographic groups. My Insights about the Appropriateness of the Design  The prospective cohort research design employed by Batty and Hamer (2020) is appropriate, as it allowed the researchers to explore COVID-19 in relation to relevant clinical measures such as BMI and blood pressure. Further, this research design enabledthe researchers to leverage UK Biobank's data (2006-2010), which would have been ignored if methods such as cross-sectional study were used, as explained by Bell et al. (2022).  In contrast, Hillyer et al. (2021) used a cross-sectional design, which was appropriate, as it helped the researchers capture the prevalence of e-cigarette usage and associated behaviors. I also believe that the research design allowed the researchers to focus on high-risk groups (oncology patients), which would not have been easier if another design, such as prospective cohorts, had been used.  Do you agree with the researchers' conclusions? Justify your reasoning. I agree with the conclusion made by Batty and Hamer (2020) that cardiovascular risk factors such as smoking and obesity and Framing Risk Score have a positive correlation with COVID-19 hospitalization, but these findings lack generalizability outside the UK. Even though the researchers used a large sample size and prospective design to enhance their validity, the generalizability is limited to the UK, as the researchers only access UK data sources. I agree with Hillyer et al.'s (2021) conclusion that e-cigarette use among young adults (18–40 years) presents an opportunity for healthcare providers to intervene in risky behaviors (binge drinking, smoking) to reduce cancer risk. The study's findingsthat e-cigarette use is strongly associated with current smoking (RR=4.64), binge drinking (RR=3.04), and secondhand smoke exposure (RR=3.90) support the idea that these behaviors are related and increase cancer risk.  References Batty, G. D., & Hamer, M. (2020). Vascular risk factors, Framingham risk score, and COVID-19: Community-based cohort study. Cardiovascular Research, 116 (10), 16641665. https://doi.org/10.1093/cvr/cvaa178Links to an external site. Bell, E., Bryman, A., & Harley, B. (2022). Research designs. Business Research Methods. https://doi.org/10.1093/hebz/9780198869443.003.0013Links to an external site. Framingham Heart Study. (n.d.). Epidemiological background and design: The Framingham heart study. https://www.framinghamheartstudy.org/fhs-about/history/epidemiological-background/Links to an external site. Hillyer, G. C., Nazareth, M., Lima, S., Schmitt, K. M., Reyes, A., Fleck, E., Schwartz, G. K., & Terry, M. B. (2021). 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. https://doi.org/10.1007/s10900-021-01027-7Links to an external site. Let's Learn Public Health. (2017, May 5). Epidemiological studies—made easy! [Video]. YouTube. https://www.youtube.com/watch?v=Jd3gFT0-C4sLinks to an external site.