NSG 604 Module IV: Discussion 1 Wilkes University

Locate a published study utilizing one of the four descriptive study designs. Summarize the study and its findings for us. Do you feel this was an effective method for studying the topic of interest? Why or why not? Share a visual representation of the study results (one of the tables in the article) with us and explain it. Do you feel this was the best way to share the study results? Why or why not?

When you respond to your classmates, let them know if you agree or disagree and why.

Post your initial response by Wednesday at 11:59 PM EST. Respond to two students by Saturday at 11:59 PM EST. The initial discussion post and discussion responses occur on three different calendar days of each electronic week. All responses should be a minimum of 300 words, scholarly written, APA formatted (with some exceptions due to limitations in the D2L editor), and referenced.  A minimum of 2 references are required (other than the course textbook). These are not the complete guidelines for participating in discussions. Please refer to the Grading Rubric for Online Discussion found in the Course Resources module.

In April 2020, Japan limited the amount of polymerase chain reaction (PCR) testing used to detect COVID-19, even though there This upsurge caused worry that the Japanese government may have misjudged the disease’s epidemiology. To determine the prevalence of people with COVID-19, in April 2020, Doi et al. (2021) conducted a cross-sectional study in Kobe City with a population of 1,518,870. A cross-sectional study employs observational studies that analyze data from a population at a single point in time (Wang & Cheng, 2020). Kobe City, located in the center of Japan, was an ideal place to test for COVID-19.

In a prior study, Doi et al. (2020) attempted to find the prevalence of the number of people in Japan infected with SARS-CoV-2. Results indicated that of 1,000 samples, 33 were positive. The problem with that study is Doi et al. (2020) used immunoglobulin G (IgG) serology assays for test samples. Serology assays at that time were not particularly sensitive, meaning they could not always determine the disease from test samples. Since then, serology assays have become more sensitive and specific. In addition, for this present study, Doi et al. (2020) used two serology assays manufactured by different pharmaceuticals: Kurabo and Abbott.

Participants for the study were patients who attended an outpatient clinic in Kobo City between May 26 to June 7, 2020. The researchers obtained 1,000 test samples categorized by sex and decade of birth of the participants.

Results indicated that tests using the Kurabo assays produced 18 positives for SARS-CoV-2 antibody out of 1,000 samples, and Abbot assays produced two positives for SARS-CoV-2 antibody out of 1,000. Only two test samples had positive results with both Kurabo and Abbot assays, and 16 uncounted test samples had errors.

Table 1 – Sample characteristics.

Ages	Male	Test positive (Kurabo)	Test positive (Abbott)	Female	Test positive (Kurabo)	Test positive (Abbott)
Under 10-year-old	5	0	0	5	0	0
10–19	8	0	0	11	0	0
20–29	19	0	0	37	2	0
30–39	53	2	0	73	1	0
40–49	75	2	0	75	0	0
50–59	75	2	0	75	1	1
60–69	75	4	0	75	2	0
70–79	76	0	0	76	1	1
80–89	75	1	0	75	0	0
Over 90	18	0	0	19	0	0
Total	479	11	0	521	7	2

Doi et al. (2020) suspect the Kurabo assays produced several false positives, and the Abbot assays were more sensitive and specific. Compared to Doi et al. (2020) prior study prevalence of COVID-19 was much lower. The researchers suspect that COVID-19 prevalence is much higher in Kobe City than the results showed. They suspect there are probably many people undiagnosed.

At the time of this study, countries were scrambling to understand COVID-19. Although Doi et al. (2020) admit that their results were likely off, it is an effort to try and find the prevalence of COVID-19 in Kobe City, Japan. Moreover, Doi et al. (2020) research most likely helped future efforts with COVID-19 research in Japan.

Reference

Doi, A., Iwata, K., Kuroda, H., Hasuike, T., Nasu, S., Nishioka, H., Tomii, K., Morimoto, T., &   Kihara, Y. (2021). A cross-sectional follow up study to estimate seroprevalence of  coronavirus disease 2019 in Kobe, Japan. Medicine 100(48), e28066.

Wang, X. & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and  recommendations. Chest, 158(1S), S65-S71.

The study design I chose was a cross-sectional study. The research in question was to examine the effect of income disparities in the United States on influenza vaccination coverage.  The total participants were 138,679 and they used a 2014-2018 National Health Interview Survey (NHIS) of self-reported adults. The research was for whether the participants received the influenza vaccine in the last 12 months as well as their family income. They used multivariable logistic regression to obtain odd ratios. The overall key finding was that adults in lower-income-level categories had decreased odds of being given the influenza vaccine compared to adults with a total family income of greater than $100,000.  They concluded that lower income adults should be considered a health priority for increasing vaccination coverage (Gaskin et al., 2023).

The study represented the targeted population as they were using the income as the exposure variable. They used the federal poverty level (FPL) to classify the income levels. They had inclusion and exclusion criteria for the selection of the population, which is a strength in the study. The data was collected by an in-person survey. This can introduce reporting bias as adults could alter their incomes. The survey was administered by in-person surveys through a series of questionnaires. The study specified that they used trained interviewers, therefore, reducing interviewer bias. The study adjusted the income with the logistic model of regression to adjust bias due to confounding. I feel like they focused on the exposure variable, which was the income disparities in this case, the study represented their target well.

Table 1.

In this table we can appreciate that the income was divided in 5 categories, and they used the logistic regression model to adjust confounding bias. The patients had received the vaccination in the previous 12 months and the age of the population survey was between 18 and 65 years old, with more females than males. The race difference is notable as the participants were not in the same baseline, on the other hand it is understandable as they were interested in the family income. Most participants had a level of education. The geographic location was unequal as they surveyed more participants in the South and most participants were unemployed.  This may have been maybe because the young participants were in college, or the older participants were retired.

In conclusion, I believe that the evidence presented in this study is good and has good external validity. There are other many factors influencing vaccinations that we have discussed in the class already. The annual average cost of influenza to healthcare is approximately $11.2 billion (Putri et al., 2023). This is alarming as providers we must help to alleviate these disparities in health care to improve the cost of care.

References

Gaskin, C. M., Woods, D. R., Ghosh, S., Watson, S., & Huber, L. R. (2023). the effect of income disparities on influenza vaccination coverage in the United States. Public health reports, 138(1), 85–90. https://doi-org.wilkes.idm.oclc.org/10.1177/00333549211069190

Putri, W. C. W. S., Muscatello, D. J., Stockwell, M. S., & Newall, A. T. (2018). Economic burden of seasonal influenza in the United States. Vaccine, 36(27), 3960–3966. https://doi.org/10.1016/j.vaccine.2018.05.057