Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Comparing the Diagnostic Power of Chest CT Scan and RT-PCR in Diagnosis of COVID-19

Background and purpose: Proper diagnosis of patients with COVID-19 is one of the challenges in medical centers. RT-PCR is the standard and reference test in diagnosis of patients with COVID-19. This study aimed at accelerating the correct diagnosis of COVID-19 and the diagnostic power of chest CT scan and RT-PCR. Materials and methods: This study was performed in 569 patients with COVID-19 admitted to Golestan-Kermanshah Hospital based on diagnostic chest CT scan. The RT-PCR test was considered as the standard and reference test in diagnosis of COVID-19. Relevant information was extracted from patients' records using a researcher-made checklist. Data analysis was performed in STATA V14. Results: The mean age of patients was 52.53±16.88 years. Men included 432 (75.9%) patients. In this study, 84% had positive chest CT scan and 84.9% had positive RT-PCR results. Sensitivity, specificity, and accuracy of chest CT scan were 88.7%, 64.8%, and 80.1%, respectively. The sensitivity of chest CT scan was 89.9% in people under 60 years of age and 82.8% in patients over 60 years old. The accuracy of chest CT scan was 84.7% in women and 78.7% in men. Conclusion: The accuracy of chest CT scan is high in patients with COVID-19 while it cannot definitively detect or rule out COVID-19. Nevertheless, it can be used as a quick tool to classify patients into positive and negative groups

Predictors of care burden among caregivers of patients with COVID‐19

Abstract Aim To explore predictors of care burden among the caregivers of patients with COVID‐19. Design The findings of this cross‐sectional study were presented in accordance with the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology statement. Methods The samples included 172 caregivers in Imam Reza and Farabi Hospitals, located in Kermanshah, Iran, who were enrolled in the study using convenience sampling. A demographic information form and the Caregiver Burden Inventory were administered. The data were collected between 13 May 202 and 20 August 2021. Results Of the caregivers, 62.8% (n = 108) were male and 71.5% (n = 123) were over 40 years old. Furthermore, 66.3% (n = 114) of caregivers had severe and very severe care burden, with a mean care burden of 78.9 ± 20.4 out of 120. A statistically significant difference was found between care burden and the variables of monthly income, health status, number of patients under care and residence status (p < 0.05). Conclusion The caregivers experienced a high care burden, which can have harmful effects on them. Therefore, it is necessary to provide them with various forms of economic, psychological and social support

Determination Of The Factors Affecting The Survival Rate Of Patients With Lung Cancer Using Bayesian Model; Historical Cohort

Introduction: Gastric cancer is one of the most common and deadly cancers in Iran. Gastric cancer is highly dependent on nutritional factors and geographical location. Therefore, the aim of this study was to evaluate the effect of nutritional factors on gastric cancer in Hamadan-Iran. Method: This study was performed as a matched case-control study that each case had two controls that matched with cases in age (±5 years) and gender at Diagnostic and Treatment Center of Mahdieh in Hamedan, Iran. First and second control groups contain persons with and without family history of cancer, respectively. Information of nutritional, epidemiological and confounding variables were collected for 100 cases and 200 controls. Controls from hospital samples, friends and acquaintances of the case group were selected. Data were collected using a researcher-made questionnaire. Data were analyzed using conditional logistic regression by Bayesian method. Results: Findings showed that, compared with individuals in the case group with the family history group with factors hot food (OR=2.35, 0.95%CrI=(1.82,5.19)), black tea (OR=1.60, 0.95%CrI (1.44,1.72)) cigarettes (OR=2.13, 0.95%CrI=(1.68,2.96)), red meat (OR=4.28, 0.95%CrI=(3.11,8.37)), residence (OR=3.15, 0.95%CrI= (1.62,5.65)), fruit (OR=0.75, 0.95% CrI=(0.63,0.83)) and vegetables (OR=0.76, 0.95%CrI=(0.59,0.85)) there was a strong statistical correlation. The results were also valid for the second control group. Conclusion: The study showed that many controllable nutritional factors in Hamadan affect the incidence of gastric cancer. It is recommended that policymakers and managers inform the public about the risk factors and prevention of gastric cancer through the publication of brochures, television and newspapers

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data

Statin Use in COVID-19 Hospitalized Patients and Outcomes: A Retrospective Study

Background: Coronavirus disease 2019 (COVID-19) might affect everyone, but people with comorbidities such as hypertension and cardiovascular disease (CVD) may often have more severe complications and worse outcomes. Although vaccinations are being performed worldwide, it will take a long time until the entire population of the world is vaccinated. On the other hand, we are witnessing the emergence of new variants of this virus. Therefore, effective therapeutic approaches still need to be considered. Statins are well-known lipid-lowering drugs, but they have also anti-inflammatory and immunomodulatory effects. This study aimed to investigate the effects of statins on the survival of COVID-19 hospitalized patients. ----- Methods: This retrospective study was performed on 583 patients admitted to a highly referenced hospital in Tabas, Iran, between February 2020 and December 2020. One hundred sixty-two patients were treated with statins and 421 patients were not. Demographic information, clinical signs, and the results of laboratory, and comorbidities were extracted from patients' medical records and mortality and survival rates were assessed in these two groups. ----- Results: The results of the Cox crude regression model showed that statins reduced mortality in COVID-19 patients (HR = 0.56, 95% CI: 0.32, 0.97; p = 0.040), although this reduction was not significant in the adjusted model (HRs=0.51, 95%CI: 0.22, 1.17; p = 0.114). Using a composite outcome comprising intubation, ICU admission, and mortality, both crude (HR = 0.43; 95% CI: 0.26, 0.73; p = 0.002) and adjusted (HR = 0.57; 95% CI: 0.33, 0.99; p = 0.048) models suggested a significant protective effect of statin therapy. ----- Conclusion: Due to anti-inflammatory properties of statins, these drugs can be effective as an adjunct therapy in the treatment of COVID-19 patients

The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% 47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% 32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% 27.9-42.8] and 33.3% 25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license