Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic.
Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF).
Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001).
Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization.
Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS).
Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017.
Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters.
Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.
Background: We aimed to evaluate the effect of COVID-19 vaccines in preventing infection, hospitalization, and mortality due to COVID-19 in Isfahan Province, Iran.
Methods: Following a retrospective cohort design, data of all vaccinated individuals since the rollout of vaccination of the general population are analyzed, Mar 2020 to Aug 13, 2021. Moreover, the data of all non-vaccinated people were collected by the census method for this period. The two groups were compared concerning hospitalization and mortality using the Chi-square test. Kaplan-Meyer was also used to calculate the median interval between receiving a vaccine and outcome (hospitalization and death).
Results: Overall, 583434 people have received a second dose of a vaccine from Mar 2020 to Aug 2021, which 74% (n=433403) was Sinopharm, 18.2% (n=106027) AstraZeneca, 3.6% (n=21216) Sputnik, and 3.9% (n=22,788) Barekat. In contrast, 2,551,140 people living in the Isfahan Province did not receive a vaccine. The median interval between injection of the first dose and the hospitalization for those who received Sinopharm, AstraZeneca, Sputnik, and Barekat was 22, 61, 19, and 19 days, respectively. For unvaccinated cases, the rates of infection, hospitalization, and mortality (per 1000 population) were 69.7, 12.1, and 1.04, respectively. In contrast, for vaccinated individuals, these rates were 3.9, 1.08, and 0.09, two weeks after the second dose, respectively.
Conclusion: The highest and lowest reduction in relative risk was for those who received AstraZeneca and Sputnik, respectively.