scholarly journals Respiratory diseases in Greater Buenos Aires and meteorological variables

2011 ◽  
Vol 19 (1) ◽  
pp. 79-90 ◽  
Author(s):  
Adelia P. Alessandro
Keyword(s):  
Respiratory Diseases ◽  
Buenos Aires ◽  
Meteorological Variables

scholarly journals Impact of COVID-19 on deaths from respiratory diseases: Panel Data evidence from Chile.

2021 ◽  
Author(s):  
Claudia Barria Sandoval ◽  
Angie Mendez ◽  
Guillermo Ferreia ◽  
Maria Cecilia Toffoletto
Keyword(s):  
Panel Data ◽  
Respiratory Diseases ◽  
Variable Number ◽  
Meteorological Variables ◽  
Random Effects Models ◽  
Number Of Patients ◽  
Before And After ◽  
Negative Effect ◽  
The Relationship ◽  
Positive Effect

The COVID-19 pandemic has left other pathologies commonly present in the population in a secondary context. Therefore, it is necessary to study the evolution of these diseases in the presence or absence of COVID-19. Objectives: The present study had the following objectives: 1. To evaluate the relationship between the COVID-19 epidemic and the possible decrease in deaths from respiratory diseases in Chile. 2 Study the relationship between meteorological variables and severity of COVID-19 with respect to deaths from respiratory diseases from January 2018 to February 2021. Methods: The variable number of deaths due to respiratory diseases in Chile was analyzed considering the monthly records of meteorological variables (temperature, precipitation and humidity) in each Region of Chile and severity of COVID-19, to evaluate the mortality trend before and after the pandemic. For this, different Non-Observable Heterogeneity Models for Panel Data were used. Results: Our findings show that among the variables that affect the mortality rate from respiratory diseases, there are the number of deaths from COVID-19 that has a negative effect, the number of patients with COVID-19 in intensive care unit (ICU) that has a positive effect and the minimum temperature with a negative effect. These results are supported by the application of the panel regression with one-way random-effects models. Conclusion: This study revealed that there is an unexpected reduction in deaths from respiratory diseases in Chile in the post-pandemic period. Therefore, it can be concluded that this variable decreased with the appearance of the COVID-19 pandemic.

scholarly journals COMPARAÇÃO DE TÉCNICAS ESTATÍSTICAS PARA ANALISAR A RELAÇÃO ENTRE DOENÇAS RESPIRATÓRIAS E CONCENTRAÇÕES DE POLUENTES ATMOSFÉRICOS

2013 ◽  
Vol 35 (1) ◽  
pp. 98
Author(s):  
Angela Radünz Lazzari
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Generalized Linear Models ◽  
Respiratory Diseases ◽  
Linear Models ◽  
Ordinal Logistic Regression ◽  
Atmospheric Pollutants ◽  
Meteorological Variables ◽  
Data Set

Air pollution is a risk factor for the population health. Its harmful effects on the population are observed even when the atmospheric pollutants are within the parameters set out in specific legislation, and they develop mainly through respiratory diseases. The aim of this study was to analyze the relationship between the concentrations of air pollutants and the incidence of respiratory diseases in the city of Porto Alegre, in 2005 and 2006. Applied multiple linear regression analysis, ordinal logistic regression and generalized linear models were used in the work. The results show good adjustment by the three techniques. The ordinal logistic regression detected only positive influence of air temperature and relative humidity in hospitalizations for respiratory diseases. Multiple linear regression related negatively hospitalizations with meteorological variables and positively with the particulate matter (PM10). The generalized linear model detected negative influence of meteorological variables and positive of pollutants, tropospheric ozone (O3) and PM10 in hospitalizations. Comparing the three statistical techniques to analyze the same data set, it can be concluded that all of them had a model with good fit to the data, but the technique of generalized linear models showed higher sensitivity in capturing the influence of pollutants, except in ordinal logistic regression and multiple linear regression.

scholarly journals Analysis of the Association Between Meteorological Variables and Mortality in the Elderly Applied to Different Climatic Characteristics of the State of São Paulo, Brazil

2020 ◽  
Author(s):  
Franciele Silva de Barros ◽  
Fábio Luiz Teixeira Gonçalves ◽  
João Paulo Assis Gobo ◽  
Júlio Barboza Chiquetto ³
Keyword(s):  
Mortality Rate ◽  
Respiratory Diseases ◽  
Moving Average ◽  
Principal Component ◽  
The Elderly ◽  
The State ◽  
Decision Makers ◽  
Meteorological Variables ◽  
Thermal Perception ◽  
Climatic Characteristics

With the rising trends in elderly populations around the world, there is a growing interest in understanding how climate sensitivity is related to their thermal perception. Therefore, we analyzed the associations between mortality in the elderly due to cardiovascular (CVD) and respiratory diseases (RD) and meteorological variables, for three cities in the State of São Paulo, Brazil: Campos do Jordão, Ribeirão Preto and Santos, from 1996 to 2017. We applied the Autoregressive Model Integrated with Moving Average (ARIMA) and the Principal Component Analysis (PCA) in order to evaluate statistical associations. Results showed CVD as a major cause of mortality, particularly in the cold period, when a high mortality rate is also observed due to RD. The mortality rate was higher in Campos do Jordão and lower in Santos (and intermediate values in Ribeirão Preto). Campos do Jordão results indicate an increased probability of mortality from CVD and RD due to lower temperatures. In Ribeirão Preto, the lower relative humidity may be related to the increase in CVD and RD deaths. This study emphasizes that, even among subtropical climates, there are significant differences. Therefore, this can assist decision makers in the implementation of mitigating and adaptive measures.

scholarly journals Progression of COVID-19 under the highly restrictive measures imposed in Argentina

2021 ◽  
Author(s):  
Jose-Luis Sagripanti ◽  
Daniel R. Aquilano
Keyword(s):  
Respiratory Diseases ◽  
World Wide ◽  
Buenos Aires ◽  
Epidemiological Data ◽  
Solar Flux ◽  
Seasonal Component ◽  
Neighboring Country ◽  
The Government ◽  
Restrictive Measures

The variety and extent of non-pharmaceutical measures implemented by the government to control COVID-19 in Argentina were exceptional, making this country the best example to analyze the evolution of COVID-19 under the most stringent and longer-lasting restrictive policies- which included 119 days of strict nation-wide lock-down, 304 days of less restrictive lock-downs, and 35 days of curfews. Two of the three peaks of infection correlated with the germicidal solar flux received in Argentina, suggesting a seasonal component and a role for the virus persisting in the environment. A massive public gathering crowding the presidential square in Buenos Aires, during which nearly half of those present were without face masks, did not alter the infection curve in that city. Comparative epidemiological data (standardized per million inhabitants) shows that COVID-19 in Uruguay, a neighboring country whose capital is at a similar latitude than Buenos Aires and who did not mandate lock-downs or curfews, progressed much slower (until vaccination started) than in Argentina. The number of yearly deaths caused by respiratory diseases and influenza in Argentina before the pandemic was similar to the total number of deaths attributed to COVID-19 cumulated on April 25, 2021, more than a year after the pandemic started. The failure to detect any benefit on ameliorating COVID-19 by the long and strict nation-wide lock-downs in Argentina should raise world-wide concerns about mandating costly and ineffective restrictive measures during ongoing or future pandemics.

scholarly journals Progression of COVID-19 under the highly restrictive measures imposed in Argentina

2021 ◽  
Author(s):  
Jose-Luis Sagripanti ◽  
Daniel R. Aquilano
Keyword(s):  
Respiratory Diseases ◽  
World Wide ◽  
Buenos Aires ◽  
Epidemiological Data ◽  
Solar Flux ◽  
Seasonal Component ◽  
Neighboring Country ◽  
The Government ◽  
Restrictive Measures

Abstract The variety and extent of non-pharmaceutical measures implemented by the government to control COVID-19 in Argentina were exceptional, making this country the best example to analyze the evolution of COVID-19 under the most stringent and longer-lasting restrictive policies- which up to May 31st 2021 included 119 days of strict nation-wide lock-down, 304 days of less restrictive lock-downs, and 35 days of curfews. Two of the three peaks of infection correlated with the germicidal solar flux received in Argentina, suggesting a seasonal component and a role for the virus persisting in the environment. A massive public gathering crowding the presidential square in Buenos Aires, during which nearly half of those present were without face masks, did not alter the infection curve in that city. Comparative epidemiological data (standardized per million inhabitants) shows that COVID-19 in Uruguay, a neighboring country whose capital is at a similar latitude than Buenos Aires and who did not mandate lock-downs or curfews, progressed much slower (until vaccination started) than in Argentina. The number of yearly deaths caused by respiratory diseases and influenza in Argentina before the pandemic was similar to the total number of deaths attributed to COVID-19 cumulated on April 25, 2021, more than a year after the pandemic started. The failure to detect any benefit on ameliorating COVID-19 by the long and strict nation-wide lock-downs in Argentina should raise world-wide concerns about mandating costly and ineffective restrictive measures during ongoing or future pandemics

scholarly journals A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina

2021 ◽  
Author(s):  
Melisa Diaz Resquin ◽  
Pablo Lichtig ◽  
Diego Alessandrello ◽  
Marcelo De Oto ◽  
Darío Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Buenos Aires ◽  
Pearson Correlation ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Meteorological Variables ◽  
Data Set

Abstract. The COVID-19 (COronaVIrus Disease 2019) pandemic provided the unique opportunity to evaluate the role of a sudden and deep decline in air pollutant emissions in the ambient air of numerous cities worldwide. Argentina, in general, and the Metropolitan Area of Buenos Aires (MABA), in particular, were under strict control measures from March to May 2020. Private vehicle restrictions were intense, and primary pollutant concentrations decreased substantially. To quantify the changes in CO, NO, NO2, PM10, SO2 and O3 concentrations under the stay-at-home orders imposed against COVID-19, we compared the observations during the different lockdown phases with both observations during the same period in 2019 and concentrations that would have occurred under a business-as-usual (BAU) scenario under no restrictions. We employed a Random Forest (RF) algorithm to estimate the BAU concentration levels. This approach exhibited a high predictive performance based on only a handful of available indicators (meteorological variables, air quality concentrations and emission temporal variations) at a low computational cost. Results during testing showed that the model captured the observed daily variations and the diurnal cycles of these pollutants with a normalized mean bias (NMB) of less than 11 % and Pearson correlation coefficients of the diurnal variations of between 0.65 and 0.89 for all the pollutants considered. Based on the Random Forest results, we estimated that the lockdown implied concentration decreases of up to 47 % (CO), 60 % (NOx) and 36 % (PM10) during the strictest mobility restrictions. Higher O3 concentrations (up to 87 %) were also observed, which is consistent with the response in a VOC-limited chemical regime to the decline in NOx emissions. Relative changes with respect to the 2019 observations were consistent with those estimated with the Random Forest model, but indicated that larger decreases in primary pollutants and lower increases in O3 would have occurred. This points out to the need of accounting not only for the differences in emissions, but also in meteorological variables to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. The data set used in this study and an introductory machine learning code are openly available at https://data.mendeley.com/datasets/h9y4hb8sf8/1 (Diaz Resquin et al., 2021).

Open discussion

1982 ◽  
Vol 99 ◽  
pp. 605-613
Author(s):  
P. S. Conti
Keyword(s):  
Buenos Aires ◽  
Large Mass ◽  
List Type ◽  
Common Phenomenon ◽  
Open Discussion ◽  
The Stability ◽  
Ring Nebulae

Conti: One of the main conclusions of the Wolf-Rayet symposium in Buenos Aires was that Wolf-Rayet stars are evolutionary products of massive objects. Some questions:–Do hot helium-rich stars, that are not Wolf-Rayet stars, exist?–What about the stability of helium rich stars of large mass? We know a helium rich star of ∼40 MO. Has the stability something to do with the wind?–Ring nebulae and bubbles : this seems to be a much more common phenomenon than we thought of some years age.–What is the origin of the subtypes? This is important to find a possible matching of scenarios to subtypes.

Gut microbiome a promising target for management of respiratory diseases

2020 ◽  
Vol 477 (14) ◽  
pp. 2679-2696
Author(s):  
Riddhi Trivedi ◽  
Kalyani Barve
Keyword(s):  
Respiratory Diseases ◽  
New Technology ◽  
Bacterial Flora ◽  
Systemic Circulation ◽  
The Body ◽  
Lung Pathology ◽  
Promising Target ◽  
Current Therapies ◽  
Intestinal Microbial Flora ◽  
On Chip

The intestinal microbial flora has risen to be one of the important etiological factors in the development of diseases like colorectal cancer, obesity, diabetes, inflammatory bowel disease, anxiety and Parkinson's. The emergence of the association between bacterial flora and lungs led to the discovery of the gut–lung axis. Dysbiosis of several species of colonic bacteria such as Firmicutes and Bacteroidetes and transfer of these bacteria from gut to lungs via lymphatic and systemic circulation are associated with several respiratory diseases such as lung cancer, asthma, tuberculosis, cystic fibrosis, etc. Current therapies for dysbiosis include use of probiotics, prebiotics and synbiotics to restore the balance between various species of beneficial bacteria. Various approaches like nanotechnology and microencapsulation have been explored to increase the permeability and viability of probiotics in the body. The need of the day is comprehensive study of mechanisms behind dysbiosis, translocation of microbiota from gut to lung through various channels and new technology for evaluating treatment to correct this dysbiosis which in turn can be used to manage various respiratory diseases. Microfluidics and organ on chip model are emerging technologies that can satisfy these needs. This review gives an overview of colonic commensals in lung pathology and novel systems that help in alleviating symptoms of lung diseases. We have also hypothesized new models to help in understanding bacterial pathways involved in the gut–lung axis as well as act as a futuristic approach in finding treatment of respiratory diseases caused by dysbiosis.

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