Vaccination associated with gross domestic product and fewer deaths in countries and regions

Abstract

Background: Vaccination can have a substantial impact on mitigating COVID-19 outbreaks. However, the vaccine rollout rates associated with the gross domestic product (GDP) and few deaths are required for verification. Three hypotheses were made: the inflection point (IP) days on the cumulative number of confirmed cases (CNCCs) in 2021 are improved (i.e., shorter than 2021); the vaccinations are associated with national GDP and fewer deaths; and the association is weak between vaccinations and CNCC IP based on the argument that vaccinations are limited to protection against milder infection. Methods: The corresponding CNCCs and deaths were downloaded from the GitHub website. Four variables, including IP days on CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions, were collected. Correlation coefficients (CCs) between variables were computed to verify the association with vaccination rates. Four tasks were achieved: determining IP days for each country/region; drawing forest plots to identify the improvement in 2021; using a pyramid plot to identify the improvement in US states; and applying the Kano diagram to verify the three hypotheses mentioned above. Results: We observed that the IP days on CNCCs in 2021 were shorter than those in 2021 among continents and US states; the CCs were 0.66 ( t = 12.21, P < .01) and −0.15 ( t = 2.11, P < .01) using the paired variables of [VD100, GDP] and [VD100, deaths IP], respectively; and the CC between CNCC IP and deaths IP was 0.73 ( t = 14.84, P < .01), but a weak association with CC = −0.01 existed between CNCC IP and VD100. Conclusion: Our results indicate that vaccination has a significant effect on mitigating COVID-19 outbreaks, even with limited protection against infection. Continued compliance with nonpharmaceutical interventions is essential to the fight against COVID-19 in the future.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis

“…[66] As usual, we report the most productive AIC/R in bibliometrics. One noteworthy difference is that both China and the USA were divided into provinces/metropolitan cities and states in comparison with other countries with smaller populations for research, which is similar to previous studies [51,[67][68][69] illustrating the geographical distribution of indicators on a choropleth map for readers to easily comprehend the study results. Otherwise, the most productive countries would be located in either the United States or China.…”

The use of the time-to-event index (Tevent) to compare the negative impact of COVID-19 on public health among continents/regions in 2020 and 2021: An observational study

“…The first concern is the calculation of the Tevnet index. While the concept is somewhat different from the traditional survival analysis (e.g., backward computation of survival rates as described in Table 1), it is nonetheless similar to the traditional survival analysis (see the codes at the link [28] ). The Tevent-index may be applied to other epidemics, not only the COVID-19 pandemic, particularly if significant differences exist between falsect(citations or DCCs on the vertical axis) and falseDt(publications or infected days on the horizontal axis), referring to Equation 3.…”

“…The ImpactCOV of all Tevents in counties/regions was compared using a choropleth map [ 27 ] : darker colors indicate a higher ImpactCOV. In this study, 2 types of comparisons were performed between continents/countries using the parameter SMD with the Q-index [ 28 ] and a time-to-event analysis using a log-rank test. [ 25 ] The study’s second goal (i.e., the comparison of ImpactCOVs between continents/countries in 2020 and 2021) will be accomplished.…”

Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis

“…We made 3 comparisons in IRA between the 2 authors, including publications on the IBP, [ 39 , 40 ] keyword plus in WoSCC using the Sankey diagram, [ 41 , 42 ] and comparisons of differences in research features on the forest plot. [ 34 , 43 , 44 ]…”