Introduction
It is important to note that among the most vital skills and competencies of a health professional is the ability to critically review and analyze the scientific literature to properly understand how data was collected, processed, and interpreted. The given critical review will primarily focus on a peer-reviewed cohort epidemiological study of COVID-19 in New York City (NYC). The core aspects of the review will encompass the extensive and comprehensive evaluation of data collection, data analysis, and data interpretation in correspondence with the essential competencies stated above.
Data Collection
The context of the study takes place in New York City with the main concern with critically ill COVID-19 patients. The core objectives of the research are to understand and assess the epidemiological data of high-risk and vulnerable patients hospitalized in NYC to observe clinical outcomes and clinical course elements. It is stated: “Over 40 000 patients with COVID-19 have been hospitalized in New York City … data on the epidemiology, clinical course, and outcomes of critically ill patients with COVID-19 in this setting are needed” (Cummings et al., 2020, p. 1763). The primary exposure of interest was the COVID-19 virus, whereas the primary outcome was the rate of in-hospital death (Cummings et al., 2020). The former was highly accurately measured because all cases were laboratory-measured and confirmed for COVID-19 viral presence.
The outcome data was appropriate and reliable due to the strong basis of the connection between critical illness caused by the viral infection accompanied by the support of secondary outcomes for further data validity. These included “frequency and duration of invasive mechanical ventilation, frequency of vasopressor use and renal replacement therapy, and time to in-hospital clinical deterioration following admission” (Cummings et al., 2020, p. 1763). The type of research conducted was a prospective cohort study. It is defined as “a research study that follows over time groups of individuals who are alike in many ways but differ by a certain characteristic and compares them for a particular outcome” (National Cancer Institute, 2022, para. 1). In the case of the study of interest, the cohort can be observed in the fact that the researchers followed critically ill COVID-19 patients over time to assess the clinical outcome of hospitalization and subsequent death.
The source of the study population was two healthcare organizations. It is stated that a “cohort study took place at two New York-Presbyterian hospitals affiliated with Columbia University Irving Medical Center in northern Manhattan” (Cummings et al., 2020, p. 1764). The study’s subject selection was based on critical illness with acute hypoxemic respiratory failure, adulthood criteria (aged ≥18 years), and laboratory-confirmed COVID-19 diagnosis (Cummings et al., 2020). A total of 257 patients were included (n = 257), and there was no control or comparison group, which meant that all 257 patients were in the proposition. The bias is minimal and unlikely since the study is primarily interested in COVID-19 epidemiology in NYC, but the focus on two hospitals might have skewed the data towards the Manhattan area. The bias for the data collection was minimal and unlikely as well because the information came from the most reliable sources, such as electronic health records. The confounding factors were minimized by including them in the observation, which implies that all relevant conditions were analyzed, indicating the sufficiency of these provisions.
Data Analysis
The core methodological framework of data analysis was statistical assessment through a multivariable Cox model. Standard deviations, medians, and Kaplan-Meier cumulative incidence plots were used to control confounding during the process (Cummings et al., 2020). These methods provided a sufficient degree of assurance that the results were not skewed by confounding factors. The measures of association included sex, age, and clinical outcomes. The statistical stability was ensured by the additional inclusion of comorbidity variables.
Data Interpretation
The major results of the study were the most critically ill and hospitalized patients had obesity, and comorbidities, such as diabetes and hypertension, and they tended to be males over 60 years of age. The results were minimally affected, indicating low magnitude, and the direction of bias can be considered negligible. However, the selection bias could be impacted by the focus on two hospitals only, which might be serving the older population in general. Information bias additionally could be minimally present due to the medical standards of knowledge about comorbidities’ association with COVID-19 complications. The magnitude of nondifferential misclassification is low, whereas the direction is appropriate because the cases were confirmed through labs and reliable instruments. The authors adequately address the limitations of the study by stating the poor generalizability of the two hospitals and their demographic characteristics.
Conclusion
In sum, the main conclusion of the study is that the most vulnerable populations to COVID-19 are older male adults with comorbidities and obesity who have thrombosis and inflammation biomarkers. These findings are properly justified by the data and statistical analysis, and the data can be reliably generalized to the NYC population. However, it should be noted that generalization is a major limitation of the research, which is why it should not be used for populations outside NYC.
References
Cummings, M. J., Baldwin, M. R., Abrams, D., Jacobson, S. D., Meyer, B. J., MPhil, E. M. B., Aaron, J. G., Claassen, J., Rabbani, L. E., Hastie, J., Hochman, B. R., Salazar-Schicchi, J., Yip, N. H., Brodie, D., & O’Donnell, M. R. (2020). Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: A prospective cohort study. The Lancet, 395(10239), 1763-1770.
National Cancer Institute. (2022). Prospective cohort study.