Influence of environmental factors on the spread of COVID-19 in Saudi Arabia

Receiver operating characteristic (ROC) to determine cut-off points of clinical and biomolecular markers to discriminate mortality in severe COVID-19 living at high altitude

BACKGROUND

In 2020, Ecuador had one of the highest death rates because of COVID-19. The role of clinical and biomolecular markers in COVID disease prognosis, is still not well supported by available data. In order for these markers to have practical application in clinical decision-making regarding patient treatment and prognosis, it is necessary to know an optimal cut-off point, taking into consideration ethnic differences and geographic conditions.

AIM

To determine the value of clinical and biomolecular markers, to predict mortality of patients with severe COVID-19 living at high altitude.

METHODS

In this study, receiver operating characteristic (ROC) curves, area under the curve (AUC) of ROC, sensitivity, specificity and likelihood ratios were calculated to determine levels of clinical and biomolecular markers that best differentiate survivors versus non-survivors in severe COVID subjects that live at a high altitude setting.

RESULTS

Selected cut-off values for ferritin (≥ 1225 ng/dl, p = 0.026), IL-6 (≥ 11 pg/ml, p = 0.005) and NLR (≥ 22, p = 0.008) at 24 h, as well as PaFiO2 (≤ 164 mmHg, p = 0.015), NLR (≥ 16, p = p = 0.013) and SOFA (≥ 6, p = 0.031) at 72 h, appear to have good discriminating power to differentiate survivors versus non-survivors. Additionally, odds ratios for ferritin (OR = 3.38); IL-6 (OR = 17.07); PaFiO2 (OR = 4.61); NLR 24 h (OR = 4.95); NLR 72 h (OR = 4.46), and SOFA (OR = 3.77) indicate increased risk of mortality when cut-off points were taken into consideration.

CONCLUSIONS

We proposed a straightforward and understandable method to identify dichotomized levels of clinical and biomolecular markers that can discriminate between survivors and non-survivors patients with severe COVID-19 living at high altitudes.

COVID-19 resilience index in European Union countries based on their risk and readiness scale

Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries' COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure.

Role of meteorological parameters with the spread of Covid-19 in Pakistan: application of autoregressive distributed lag approach

This research focuses on the impacts of different meteorological parameters (temperature, humidity, rainfall, and evapotranspiration) on the transmission of Covid-19 in the administrative regions and provinces of Pakistan, i.e., Azad Jammu and Kashmir, Gilgit Baltistan, Khyber Pakhtunkhwa, Islamabad, Punjab, Sindh, and Balochistan from June 10, 2020, to August 31, 2021. This study analyzes the relation between Covid-19-confirmed cases and the meteorological parameters with the help of the autoregressive distributed lag model. In this research, additional tools (t-statistics, f-statistics, and time series analysis) are used for the motive of examining the linear relationship, the productivity of the model, and for the significant association between dependent and independent variables, lnccc and lnevp, lnhum, lnrain, lntemp, respectively. Values of t-statistics and f-statistics reveal that variables have a connection and individual significance for the model exist. Time series display that the Covid-19 spread increased from June 10, 2020, to August 31, 2021, in Pakistan. Temperature positively influenced the Covid-19-confirmed cases in all provinces of Pakistan in the long run. Evapotranspiration and rainfall influenced positively, while specific humidity influenced negatively on the confirmed Covid-19 cases in Azad Jammu Kashmir, Khyber Pakhtunkhwa, and Punjab. Specific humidity had a positive impact, while evapotranspiration and rainfall had the negative impact on the Covid-19-confirmed cases in Sindh and Balochistan. Evapotranspiration and specific humidity influenced positively, while rainfall influenced the Covid-19-confirmed cases negatively in Gilgit Baltistan. Evapotranspiration influenced positively, while specific humidity and rainfall influenced negatively on the Covid-19-confirmed cases in Islamabad.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-023-04997-4.

Analysis of potential risk factors associated with COVID-19 and hospitalization

Coronavirus disease 2019 (COVID-19) was found to cause complications in certain groups of people, leading to hospitalization. Several factors have been linked to this, such as gender, age, comorbidity, and race. Understanding the precise reasons for the COVID-19-induced complications might help in designing strategies to minimize hospitalization. A retrospective, cross-sectional observational study was conducted for patients in a COVID-19-designated specialty hospital after obtaining ethical clearance. Patients' demographic and clinical characteristics, such as age, gender, race, vaccinated status, complications, comorbidities, and medications, were retrieved from the hospital medical database. The data were statistically analyzed to determine the association between the predictors and the outcomes of COVID-19. An odds ratio (both unadjusted and adjusted) analysis was carried out to determine the risk factors for hospitalization [non-intensive care (non-ICU) and intensive care (ICU)] due to COVID-19. The data from the study indicated that the majority of patients hospitalized due to COVID-19 were male (>55%), aged > 60 years (>40%), married (>80%), and unvaccinated (>71%). The common symptoms, complications, comorbidities, and medications were fever, pneumonia, hypertension, and prednisolone, respectively. Male gender, patients older than 60 years, unemployed, unvaccinated, complicated, and comorbid patients had an odds ratio of more than 2 and were found to be significantly (p < 0.05) higher in ICU admission. In addition, administration of prednisolone and remdesivir was found to significantly reduce (p < 0.05) the odds ratio in ICU patients. The analysis of the data suggested that male gender, age above 60 years, and unvaccinated with comorbidities increased the complications and resulted in hospitalization, including ICU admission. Hypertension and type 2 diabetes associated with obesity as metabolic syndrome could be considered one of the major risk factors. Preventive strategies need to be directed toward these risk factors to reduce the complications, as well as hospitalization to defeat the COVID-19 pandemic.