Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

A sustainable model for pediatric oncology nursing education and capacity building in Latin American hospitals: Evolution and impact of a nurse educator network.

Through an "educate-the-educator" twinning model, pediatric oncology nurse educator roles and programs have been established at hospitals in Latin America since 2008. However, with increasing demand for nurse educator programs in the region, a twinning approach was no longer sustainable. Thus, a "nurse educator network" approach was established to scale adaptable, standardized multisite education and quality initiatives. The development, expansion, and impact of a sustainable network approach for pediatric oncology nursing capacity building in Latin America is described. The educator network approach serves as a potential model for other geographical regions. Coronavirus disease 2019 (COVID-19) impact and adaptations are addressed.

Capacity of transportation and spread of COVID-19-an ironical fact for developed countries.

The widespread epidemic of the COVID-19 in developed countries such as Europe and the USA has sparked many speculations. What factors caused the rapid early pandemic of the COVID-19 in developed countries is the main goal of this study. We collected the main disease indicators and various environmental and economic factors in 61 countries around the world. Our results show that the number of cases is positively correlated with the country's GDP. We further analyzed the factors related to the spread of the disease. They indicate a strong positive correlation between the total patient numbers and the number of airline passengers, with an r value of 0.80. There is also a positive correlation between the number of car ownership and the total patient, with an r value of 0.35. Both the flight passengers and car ownership contribute 66% to the number of total patients. The total death numbers and the number of airline passengers are positively correlated, with an r value of 0.71. A positive correlation between the number of car ownership and the total deaths is with an r value of 0.42. The total contribution of both the flight passengers and car ownership to the number of total deaths is 57%. Our conclusion is that the main cause of the coronavirus pandemic in developed countries is related to the transportation. In other words, the number of travelers determined the early coronavirus pandemic. Therefore, it is necessary to strengthen restrictions and screening of passengers at airports, especially international airports.

COVID-19 vaccine: Call for employees in international transportation industries and international travelers as the first priority in global distribution.

While countries are in a hurry to obtain SARS-CoV-2 vaccine, we are concerned with the availability of vaccine and whether a vaccine will be available to all in need. We predicted three possible scenarios for vaccine distributions and urge an international united action on the worldwide equitable access. In case the international community does not reach a consensus on how to distribute the vaccine to achieve worldwide equitable access, we call for a distribution plan that includes the employees in international transportation industries and international travelers to halt the disease transmission and promote the recovery of the global economy.

Development of a COVID-19 alternate care site from ground zero: A nursing perspective.

The novel coronavirus disease SARS-CoV-2 (COVID-19) outbreak rapidly generated an unprecedented global, national, and state public health crisis with the need to rapidly develop alternate care sites (ACS) to care for COVID-19 patients within an overburdened health care system. A hospital care model ACS to increase the health care capacity, provide care for mild to moderately symptomatic patients, and offer local self-sustainment for a surge of patients was developed in Memphis, Tennessee located in Shelby County. We completed a temporary conversion of a large unused newspaper publication building to a health care facility for COVID-19 patients. Developing an ACS from ground zero was met with many challenges, and throughout the process important lessons were learned. With the goal to complete the building conversion within a 28-day timeframe, collaboration among the numerous governmental, health care, and private agencies was critical and nursing leadership was key to this process. The purpose of this paper is to describe the development of a COVID-19 ACS in Memphis, TN, which has a large at-risk population with limited access to health care. Specifically, we will discuss the strong leadership role of nursing faculty, key challenges, and lessons learned, as well as provide checklists and models for others in similar circumstances.

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.